code: 
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=2_5_6	)			->							int:
  				A__     :							List[Any]   				=min(img.size	)
  
  				if s < target_image_size:
  								raise ValueError(f'min dim for image {s} < {target_image_size}'	)
  
  				A__     :							Optional[int]   				=target_image_size / s
  				A__     :							List[str]   				=(round(r * img.size[1]	), round(r * img.size[0]	))
  				A__     :							Union[str, Any]   				=TF.resize(snake_case_,	snake_case_,	interpolation=PIL.Image.LANCZOS	)
  				A__     :							Any   				=TF.center_crop(snake_case_,	output_size=2 * [target_image_size]	)
  				A__     :							Optional[int]   				=torch.unsqueeze(T.ToTensor()(snake_case_	),	0	)
  				return img
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				A__     :							Tuple   				=2.0 * x - 1.0
  				return x
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=True,	snake_case_="RGB"	)			->							Optional[Any]:
  				A__     :							Optional[Any]   				=x.detach().cpu()
  				if process:
  								A__     :							Union[str, Any]   				=post_process_tensor(snake_case_	)
  				A__     :							List[Any]   				=x.numpy()
  				if process:
  								A__     :							Any   				=(2_5_5 * x).astype(np.uinta	)
  				A__     :							str   				=Image.fromarray(snake_case_	)
  				if not x.mode == mode:
  								A__     :							Tuple   				=x.convert(snake_case_	)
  				return x
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Optional[Any]:
  				A__     :							Any   				=torch.clamp(snake_case_,	-1.0,	1.0	)
  				A__     :							Tuple   				=(x + 1.0) / 2.0
  				A__     :							Optional[Any]   				=x.permute(1,	2,	0	)
  				return x
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Any:
  				A__     :							Dict   				=post_process_tensor(x.squeeze()	)
  				return x.permute(2,	0,	1	).unsqueeze(0	)
  
  
  
  
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      (      UpperCamelCase_   ):
  		__lowercase													=		{
  		    """sld_warmup_steps""": 15,
  		    """sld_guidance_scale""": 20,
  		    """sld_threshold""": 0.0,
  		    """sld_momentum_scale""": 0.0,
  		    """sld_mom_beta""": 0.0,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 10,
  		    """sld_guidance_scale""": 1_000,
  		    """sld_threshold""": 0.01,
  		    """sld_momentum_scale""": 0.3,
  		    """sld_mom_beta""": 0.4,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 7,
  		    """sld_guidance_scale""": 2_000,
  		    """sld_threshold""": 0.0_25,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  		__lowercase													=		{
  		    """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      (      UpperCamelCase_   ):
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  
  
  if is_transformers_available() and is_torch_available():
  						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  						from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  