





from __future__ import annotations



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				bool:
 UpperCamelCase			:      Optional[int] 			=     get_failure_array(_lowerCAmelCase  )

 # 2) Step through text searching for pattern
 UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     0, 0  # index into text, pattern
 while i < len(_lowerCAmelCase  ):
  if pattern[j] == text[i]:
   if j == (len(_lowerCAmelCase  ) - 1):
    return True
   j += 1

  # if this is a prefix in our pattern
  # just go back far enough to continue
  elif j > 0:
   UpperCamelCase			:      str 			=     failure[j - 1]
   continue
  i += 1
 return False



def 	A_       (  _lowerCAmelCase  )		->				list[int]:
 UpperCamelCase			:      List[Any] 			=     [0]
 UpperCamelCase			:      List[Any] 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     1
 while j < len(_lowerCAmelCase  ):
  if pattern[i] == pattern[j]:
   i += 1
  elif i > 0:
   UpperCamelCase			:      Optional[int] 			=     failure[i - 1]
   continue
  j += 1
  failure.append(_lowerCAmelCase  )
 return failure


if __name__ == "__main__":
    # Test 1)
    __lowerCamelCase :  Optional[Any]										=  """abc1abc12"""
    __lowerCamelCase :  int										=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
    __lowerCamelCase :  Tuple										=  """alskfjaldsk23adsfabcabc"""
    assert kmp(pattern, texta) and not kmp(pattern, texta)

    # Test 2)
    __lowerCamelCase :  List[Any]										=  """ABABX"""
    __lowerCamelCase :  Dict										=  """ABABZABABYABABX"""
    assert kmp(pattern, text)

    # Test 3)
    __lowerCamelCase :  Optional[int]										=  """AAAB"""
    __lowerCamelCase :  Any										=  """ABAAAAAB"""
    assert kmp(pattern, text)

    # Test 4)
    __lowerCamelCase :  Optional[Any]										=  """abcdabcy"""
    __lowerCamelCase :  Optional[Any]										=  """abcxabcdabxabcdabcdabcy"""
    assert kmp(pattern, text)

    # Test 5)
    __lowerCamelCase :  str										=  """aabaabaaa"""
    assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]







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

import torch_xla.distributed.xla_multiprocessing as xmp



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      Tuple 			=     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=_lowerCAmelCase							,     default=1							,     help="Number of TPU cores to use (1 or 8)."  )

 # positional
 parser.add_argument(
     "training_script"							,     type=_lowerCAmelCase							,     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=_lowerCAmelCase  )

 return parser.parse_args()



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     parse_args()

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

 # Patch sys.argv
 UpperCamelCase			:      List[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()
