
'''simple docstring'''


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 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							42
   _snake_case		   =							42


if is_transformers_available() and is_torch_available():
   from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline







'''simple docstring'''


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def  __snake_case     (    ):
   UpperCamelCase       =  ArgumentParser(
       description=(
           '''PyTorch TPU distributed training launch '''
           '''helper utility that will spawn up '''
           '''multiple distributed processes'''
       ))

   # Optional arguments for the launch helper
   parser.add_argument('''--num_cores''',      type=_UpperCAmelCase,      default=1,      help='''Number of TPU cores to use (1 or 8).''')

   # positional
   parser.add_argument(
       '''training_script''',      type=_UpperCAmelCase,      help=(
           '''The full path to the single TPU training '''
           '''program/script to be launched in parallel, '''
           '''followed by all the arguments for the '''
           '''training script'''
       ),      )

   # rest from the training program
   parser.add_argument('''training_script_args''',      nargs=_UpperCAmelCase)

   return parser.parse_args()



def  __snake_case     (    ):
   UpperCamelCase       =  parse_args()

   # Import training_script as a module.
   UpperCamelCase       =  Path(args.training_script)
   sys.path.append(str(script_fpath.parent.resolve()))
   UpperCamelCase       =  script_fpath.stem
   UpperCamelCase       =  importlib.import_module(_UpperCAmelCase)

   # Patch sys.argv
   UpperCamelCase       =  [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores)]
   xmp.spawn(mod._mp_fn,      args=(),      nprocs=args.num_cores)


if __name__ == "__main__":
   main()





