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_           =				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
UpperCAmelCase_           =				[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]




class 			__magic_name__	:





   """simple docstring"""




   def __init__(						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			WATERMARK_BITS
    _UpperCamelCase:  Optional[Any]    						=			WatermarkEncoder()

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



   def 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	torch.FloatTensor				):



    """simple docstring"""



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

    _UpperCamelCase:  List[str]    						=			(255 * (images / 2 + 0.5)).cpu().permute(0		,	2		,	3		,	1				).float().numpy()

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

    _UpperCamelCase:  int    						=			torch.from_numpy(np.array(_lowercase				)				).permute(0		,	3		,	1		,	2				)

    _UpperCamelCase:  str    						=			torch.clamp(2 * (images / 255 - 0.5)		,	min=-1.0		,	max=1.0				)
    return images
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_           =				{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				[
      '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MegaForCausalLM''',
      '''MegaForMaskedLM''',
      '''MegaForMultipleChoice''',
      '''MegaForQuestionAnswering''',
      '''MegaForSequenceClassification''',
      '''MegaForTokenClassification''',
      '''MegaModel''',
      '''MegaPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

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