


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()










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






