





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

import torch_xla.distributed.xla_multiprocessing as xmp
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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=_lowercase	,						default=1	,						help='''Number of TPU cores to use (1 or 8).'''		)

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

						return parser.parse_args()
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							parse_args()

						# Import training_script as a module.
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							Path(args.training_script		)
						sys.path.append(str(script_fpath.parent.resolve()		)		)
						SCREAMING_SNAKE_CASE							:					Dict           =							script_fpath.stem
						SCREAMING_SNAKE_CASE							:					List[str]           =							importlib.import_module(_lowercase		)

						# Patch sys.argv
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[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()






from __future__ import annotations

from typing import Any





class       lowercase__		(     UpperCamelCase_):
						pass






class       lowercase__		:


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							data
												SCREAMING_SNAKE_CASE							:					Node | None           =							None


						def __iter__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self
												SCREAMING_SNAKE_CASE							:					Tuple           =							[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(UpperCamelCase__		)
																		yield node.data
																		SCREAMING_SNAKE_CASE							:					Dict           =							node.next_node



						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												try:
																		list(self		)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
					__UpperCamelCase	: List[Any]    		=  Node(1)
					__UpperCamelCase	: str    		=  Node(2)
					__UpperCamelCase	: Dict    		=  Node(3)
					__UpperCamelCase	: List[Any]    		=  Node(4)
					print(root_node.has_loop)  # False
					__UpperCamelCase	: int    		=  root_node.next_node
					print(root_node.has_loop)  # True

					__UpperCamelCase	: Union[str, Any]    		=  Node(5)
					__UpperCamelCase	: Union[str, Any]    		=  Node(6)
					__UpperCamelCase	: List[Any]    		=  Node(5)
					__UpperCamelCase	: List[str]    		=  Node(6)
					print(root_node.has_loop)  # False

					__UpperCamelCase	: List[Any]    		=  Node(1)
					print(root_node.has_loop)  # False





