


from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase__			:      Dict								={'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
				from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
				import sys

				lowerCAmelCase__			:      Optional[Any]								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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__			:      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
lowerCAmelCase__			:      str								=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					WATERMARK_BITS
										SCREAMING_SNAKE_CASE_		:     int					=					WatermarkEncoder()

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if images.shape[-1] < 2_5_6:
													return images

										SCREAMING_SNAKE_CASE_		:     Tuple					=					(2_5_5 * (images / 2 + 0.5)).cpu().permute(0			,						2			,						3			,						1       ).float().numpy()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.encoder.encode(lowerCAmelCase__			,						'dwtDct'       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.from_numpy(np.array(lowerCAmelCase__       )       ).permute(0			,						3			,						1			,						2       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(2 * (images / 2_5_5 - 0.5)			,						min=-1.0			,						max=1.0       )
										return images





