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
_UpperCAmelCase            =		0B1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_UpperCAmelCase            =		[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self ):



						"""simple docstring"""





						A_		:    Tuple         =				WATERMARK_BITS
						A_		:    Tuple         =				WatermarkEncoder()

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







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





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

						A_		:    Union[str, Any]         =				(2_5_5 * (images / 2 + 0.5)).cpu().permute(0		,     2		,     3		,     1 ).float().numpy()

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

						A_		:    Union[str, Any]         =				torch.from_numpy(np.array(_lowerCamelCase ) ).permute(0		,     3		,     1		,     2 )

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase            =		{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		["""BridgeTowerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BridgeTowerForContrastiveLearning""",
	    """BridgeTowerForImageAndTextRetrieval""",
	    """BridgeTowerForMaskedLM""",
	    """BridgeTowerModel""",
	    """BridgeTowerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase            =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






