code: 
  
  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     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Optional[int]    				=							{
  	    '''sld_warmup_steps''': 15,
  	    '''sld_guidance_scale''': 20,
  	    '''sld_threshold''': 0.0,
  	    '''sld_momentum_scale''': 0.0,
  	    '''sld_mom_beta''': 0.0,
  	}
  	_lowercase		:      List[Any]    				=							{
  	    '''sld_warmup_steps''': 10,
  	    '''sld_guidance_scale''': 1000,
  	    '''sld_threshold''': 0.01,
  	    '''sld_momentum_scale''': 0.3,
  	    '''sld_mom_beta''': 0.4,
  	}
  	_lowercase		:      List[Any]    				=							{
  	    '''sld_warmup_steps''': 7,
  	    '''sld_guidance_scale''': 2000,
  	    '''sld_threshold''': 0.0_25,
  	    '''sld_momentum_scale''': 0.5,
  	    '''sld_mom_beta''': 0.7,
  	}
  	_lowercase		:      Dict    				=							{
  	    '''sld_warmup_steps''': 0,
  	    '''sld_guidance_scale''': 5000,
  	    '''sld_threshold''': 1.0,
  	    '''sld_momentum_scale''': 0.5,
  	    '''sld_mom_beta''': 0.7,
  	}
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Union[List[PIL.Image.Image], np.ndarray]
  	_lowercase		:      Optional[List[bool]]
  	_lowercase		:      Optional[Union[List[PIL.Image.Image], np.ndarray]]
  	_lowercase		:      Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  					from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  					from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      {
      '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
      '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
  }
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				List[str]		   =      [
  			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''ProphetNetDecoder''',
  			    '''ProphetNetEncoder''',
  			    '''ProphetNetForCausalLM''',
  			    '''ProphetNetForConditionalGeneration''',
  			    '''ProphetNetModel''',
  			    '''ProphetNetPreTrainedModel''',
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
  			from .tokenization_prophetnet import ProphetNetTokenizer
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_prophetnet import (
  						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    ProphetNetDecoder,
  						    ProphetNetEncoder,
  						    ProphetNetForCausalLM,
  						    ProphetNetForConditionalGeneration,
  						    ProphetNetModel,
  						    ProphetNetPreTrainedModel,
  						)
  
  else:
  			import sys
  
  			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  