code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase					 =	{
      '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
  }
  
  try:
  	if not is_vision_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	['''ImageGPTFeatureExtractor''']
  	lowerCAmelCase					 =	['''ImageGPTImageProcessor''']
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''ImageGPTForCausalImageModeling''',
  	    '''ImageGPTForImageClassification''',
  	    '''ImageGPTModel''',
  	    '''ImageGPTPreTrainedModel''',
  	    '''load_tf_weights_in_imagegpt''',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig
  
  	try:
  		if not is_vision_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
  		from .image_processing_imagegpt import ImageGPTImageProcessor
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_imagegpt import (
  		    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    ImageGPTForCausalImageModeling,
  		    ImageGPTForImageClassification,
  		    ImageGPTModel,
  		    ImageGPTPreTrainedModel,
  		    load_tf_weights_in_imagegpt,
  		)
  
  else:
  	import sys
  
  	lowerCAmelCase					 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  