

"""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			(						):
	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=snake_case__		, default=1		, help='Number of TPU cores to use (1 or 8).'       )

	# positional
	parser.add_argument(
	    'training_script'		, type=snake_case__		, 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=snake_case__       )

	return parser.parse_args()






def  _snake_case			(						):
	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(snake_case__       )

	# 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()

"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .image_encoder import PaintByExampleImageEncoder
		from .pipeline_paint_by_example import PaintByExamplePipeline