code: from ..utils import DummyObject, requires_backends
  
  
  
  
  class      lowercase_						(						metaclass=UpperCamelCase_		):
  	UpperCAmelCase_						:      Tuple     =       ["""torch""", """transformers""", """invisible_watermark"""]
  
  
  	def __init__(							self	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )		->List[Any]:
  								requires_backends(self	,    ['''torch''', '''transformers''', '''invisible_watermark''']       )
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )		->Optional[int]:
  								requires_backends(cls	,    ['''torch''', '''transformers''', '''invisible_watermark''']       )
  
  
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )		->Optional[Any]:
  								requires_backends(cls	,    ['''torch''', '''transformers''', '''invisible_watermark''']       )
  
  
  
  
  class      lowercase_						(						metaclass=UpperCamelCase_		):
  	UpperCAmelCase_						:      Dict     =       ["""torch""", """transformers""", """invisible_watermark"""]
  
  
  	def __init__(							self	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )		->Any:
  								requires_backends(self	,    ['''torch''', '''transformers''', '''invisible_watermark''']       )
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )		->str:
  								requires_backends(cls	,    ['''torch''', '''transformers''', '''invisible_watermark''']       )
  
  
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls	,    *__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )		->Any:
  								requires_backends(cls	,    ['''torch''', '''transformers''', '''invisible_watermark''']       )
  
  
  
  
style_context: from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  
  from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available
  
  
  
  
  @dataclass
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Union[List[PIL.Image.Image], np.ndarray]
  
  
  if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
  	from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
  	from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
  
  
  
  