



"""simple docstring"""
__a           =							[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				str		,	snake_case:				Any		)  ->      Union[str, Any]:
											snake_case_					:int		=     name
											snake_case_					:int		=     []
											snake_case_					:Any		=     []






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				Any		)  ->      List[str]:
											self.inbound.append(snake_case		)






					def   lowerCAmelCase_							(			self:				List[str]		,	snake_case:				Optional[int]		)  ->      str:
											self.outbound.append(snake_case		)






					def __repr__(			self:				Union[str, Any]		)  ->      Union[str, Any]:
											return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""

def   A_    (     _lowercase,   _lowercase=3,   _lowercase=0.85      ):







						'''simple docstring'''


						snake_case_					:List[Any]		=     {}
						for node in nodes:
												snake_case_					:Optional[int]		=     1

						snake_case_					:List[Any]		=     {}
						for node in nodes:
												snake_case_					:List[str]		=     len(node.outbound      )

						for i in range(_lowercase      ):
												print(f"""======= Iteration {i + 1} ======="""      )
												for _, node in enumerate(_lowercase      ):
																		snake_case_					:List[str]		=     (1 - d) + d * sum(
																		    ranks[ib] / outbounds[ib] for ib in node.inbound      )
												print(_lowercase      )
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     list(input("""Enter Names of the Nodes: """      ).split()      )

						snake_case_					:Tuple		=     [Node(_lowercase      ) for name in names]

						for ri, row in enumerate(_lowercase      ):
												for ci, col in enumerate(_lowercase      ):
																		if col == 1:
																								nodes[ci].add_inbound(names[ri]      )
																								nodes[ri].add_outbound(names[ci]      )

						print("""======= Nodes ======="""      )
						for node in nodes:
												print(_lowercase      )

						page_rank(_lowercase      )


if __name__ == "__main__":
						main()





"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:str		=     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_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

						# Import training_script as a module.
						snake_case_					:str		=     Path(args.training_script      )
						sys.path.append(str(script_fpath.parent.resolve()      )      )
						snake_case_					:Dict		=     script_fpath.stem
						snake_case_					:int		=     importlib.import_module(_lowercase      )

						# Patch sys.argv
						snake_case_					:Optional[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()
