code: 
  
  
  import json
  import logging
  import os
  import subprocess
  from argparse import ArgumentParser
  
  
  lowerCAmelCase					 =	logging.getLogger(__name__)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[str]:
  		__lowercase=    ArgumentParser()
  		__lowercase=    parser.parse_known_args()
  		for arg in unknown:
  				if arg.startswith(('-', '--')			):
  						parser.add_argument(arg.split('='			)[0]			)
  
  		return parser.parse_args()
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Optional[Any]:
  		__lowercase=    parse_args()
  		__lowercase=    8_8_8_8
  		__lowercase=    int(os.environ['SM_NUM_GPUS']			)
  		__lowercase=    json.loads(os.environ['SM_HOSTS']			)
  		__lowercase=    len(snake_case__			)
  		__lowercase=    os.environ['SM_CURRENT_HOST']
  		__lowercase=    hosts.index(snake_case__			)
  		__lowercase=    'INFO'
  
  		if num_nodes > 1:
  				__lowercase=    F'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'
  		else:
  				__lowercase=    F'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'
  		try:
  				subprocess.run(snake_case__     ,				shell=snake_case__			)
  		except Exception as e:
  				logger.info(snake_case__			)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
  
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
  
  
  
  
  
  
  