

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

import torch_xla.distributed.xla_multiprocessing as xmp

def      a__	(						)    ->		str:
  UpperCAmelCase			:				List[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=UpperCAmelCase							,					default=1							,					help='''Number of TPU cores to use (1 or 8).'''     )

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

  return parser.parse_args()

def      a__	(						)    ->		Optional[Any]:
  UpperCAmelCase			:				Union[str, Any]										=					parse_args()

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

  # Patch sys.argv
  UpperCAmelCase			:				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()









from string import ascii_uppercase



def      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     str    ,   snake_case						:     bool = False    ,   snake_case						:     str = ascii_uppercase			)							->				str:
   __lowerCamelCase:					List[str]	 =						keyword.upper()
   __lowerCamelCase:					Tuple	 =						plaintext.upper()
   __lowerCamelCase:					Optional[Any]	 =						set(snake_case			)

   # create a list of unique characters in the keyword - their order matters
   # it determines how we will map plaintext characters to the ciphertext
   __lowerCamelCase:					Tuple	 =						[]
   for char in keyword:
      if char in alphabet_set and char not in unique_chars:
         unique_chars.append(snake_case			)
    # the number of those unique characters will determine the number of rows
   __lowerCamelCase:					Tuple	 =						len(snake_case			)

   # create a shifted version of the alphabet
   __lowerCamelCase:					List[str]	 =						unique_chars + [
       char for char in alphabet if char not in unique_chars
   ]

   # create a modified alphabet by splitting the shifted alphabet into rows
   __lowerCamelCase:					List[str]	 =						[
       shifted_alphabet[k : k + num_unique_chars_in_keyword]
       for k in range(0    ,   26    ,   snake_case			)
   ]

   # map the alphabet characters to the modified alphabet characters
   # going 'vertically' through the modified alphabet - consider columns first
   __lowerCamelCase:					Union[str, Any]	 =						{}
   __lowerCamelCase:					str	 =						0
   for column in range(snake_case			):
      for row in modified_alphabet:
         # if current row (the last one) is too short, break out of loop
         if len(snake_case			) <= column:
            break

         # map current letter to letter in modified alphabet
         __lowerCamelCase:					Union[str, Any]	 =						row[column]
         letter_index += 1

   if verbose:
      print(snake_case			)
   # create the encrypted text by mapping the plaintext to the modified alphabet
   return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
   # example use
   print(mixed_keyword('''college''', '''UNIVERSITY'''))


