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 				__SCREAMING_SNAKE_CASE   (			UpperCAmelCase__   ):
        A				:							Optional[int]    							=     {
            'sld_warmup_steps': 15,
            'sld_guidance_scale': 20,
            'sld_threshold': 0.0,
            'sld_momentum_scale': 0.0,
            'sld_mom_beta': 0.0,
        }
        A				:							List[Any]    							=     {
            'sld_warmup_steps': 10,
            'sld_guidance_scale': 1000,
            'sld_threshold': 0.01,
            'sld_momentum_scale': 0.3,
            'sld_mom_beta': 0.4,
        }
        A				:							Dict    							=     {
            'sld_warmup_steps': 7,
            'sld_guidance_scale': 2000,
            'sld_threshold': 0.025,
            'sld_momentum_scale': 0.5,
            'sld_mom_beta': 0.7,
        }
        A				:							Optional[int]    							=     {
            'sld_warmup_steps': 0,
            'sld_guidance_scale': 5000,
            'sld_threshold': 1.0,
            'sld_momentum_scale': 0.5,
            'sld_mom_beta': 0.7,
        }
  
  
  
  
  
  
  @dataclass
  class 				__SCREAMING_SNAKE_CASE   (			UpperCAmelCase__   ):
        A				:							Union[List[PIL.Image.Image], np.ndarray]
        A				:							Optional[List[bool]]
        A				:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
        A				:							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
  
  
  __a   				=      {
      '''configuration_decision_transformer''': [
          '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''DecisionTransformerConfig''',
      ],
  }
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DecisionTransformerGPT2Model''',
        '''DecisionTransformerGPT2PreTrainedModel''',
        '''DecisionTransformerModel''',
        '''DecisionTransformerPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DecisionTransformerConfig,
    )
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  