




'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      str    					=						[0] * len(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      int    					=						[]
      SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						0

      for values in graph.values():
            for i in values:
                  indegree[i] += 1

      for i in range(len(__UpperCamelCase    )    ):
            if indegree[i] == 0:
                  queue.append(__UpperCamelCase    )

      while queue:
            SCREAMING_SNAKE_CASE   :      int    					=						queue.pop(0    )
            cnt += 1
            topo.append(__UpperCamelCase    )
            for x in graph[vertex]:
                  indegree[x] -= 1
                  if indegree[x] == 0:
                        queue.append(__UpperCamelCase    )

      if cnt != len(__UpperCamelCase    ):
            print('Cycle exists'    )
      else:
            print(__UpperCamelCase    )


# Adjacency List of Graph
UpperCamelCase_    	=				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}





topological_sort(graph)









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

import torch_xla.distributed.xla_multiprocessing as xmp





def  __magic_name__  (				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     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  __magic_name__  (				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     parse_args()

							# Import training_script as a module.
							lowercase_	:   List[Any]  				=     Path(args.training_script				)
							sys.path.append(str(script_fpath.parent.resolve()				)				)
							lowercase_	:   Tuple  				=     script_fpath.stem
							lowercase_	:   List[str]  				=     importlib.import_module(lowercase				)

							# Patch sys.argv
							lowercase_	:   Optional[int]  				=     [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()