


A_    :					Dict             ="""
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
"""

A_    :					List[Any]             =[{"""type""": """code""", """content""": INSTALL_CONTENT}]



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

import torch_xla.distributed.xla_multiprocessing as xmp




def   lowerCamelCase_       (					):



						"""simple docstring"""




						a_					=		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   lowerCamelCase_       (					):



						"""simple docstring"""




						a_					=		parse_args()

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

						# Patch sys.argv
						a_					=		[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()