






"""simple docstring"""





import functools
from math import sqrt
from time import time






def 							A(    snake_case_		,  *snake_case_		,  **snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[int]         =     time()
       lowercase__:							int         =     func(*snake_case_		,  **snake_case_						)
       lowercase__:							Tuple         =     time()
       if int(end - start						) > 0:
              print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""						)
       else:
              print(F"""{func.__name__} runtime: {(end - start) * 1000:0.4f} ms"""						)
       return output






def 							A(    snake_case_						):
       """simple docstring"""





       if n < 0:
              raise Exception("n is negative"						)
       if n == 0:
              return [0]
       lowercase__:							Any         =     [0, 1]
       for _ in range(n - 1						):
              fib.append(fib[-1] + fib[-2]						)
       return fib






def 							A(    snake_case_						):
       """simple docstring"""





       def fib_recursive_term(snake_case_						) -> int:
              if i < 0:
                     raise Exception("n is negative"						)
              if i < 2:
                     return i
              return fib_recursive_term(i - 1						) + fib_recursive_term(i - 2						)

       if n < 0:
              raise Exception("n is negative"						)
       return [fib_recursive_term(snake_case_						) for i in range(n + 1						)]






def 							A(    snake_case_						):
       """simple docstring"""





       @functools.cache
       def fib_recursive_term(snake_case_						) -> int:
              if i < 0:
                     raise Exception("n is negative"						)
              if i < 2:
                     return i
              return fib_recursive_term(i - 1						) + fib_recursive_term(i - 2						)

       if n < 0:
              raise Exception("n is negative"						)
       return [fib_recursive_term(snake_case_						) for i in range(n + 1						)]






def 							A(    snake_case_						):
       """simple docstring"""





       if n < 0:
              raise Exception("n is negative"						)
       # Cache must be outside recursuive function
       # other it will reset every time it calls itself.
       lowercase__:							dict[int, int]         =     {0: 0, 1: 1, 2: 1}  # Prefilled cache

       def rec_fn_memoized(snake_case_						) -> int:
              if num in cache:
                     return cache[num]

              lowercase__:							Any         =     rec_fn_memoized(num - 1						) + rec_fn_memoized(num - 2						)
              lowercase__:							List[str]         =     value
              return value

       return [rec_fn_memoized(snake_case_						) for i in range(n + 1						)]






def 							A(    snake_case_						):
       """simple docstring"""





       if n < 0:
              raise Exception("n is negative"						)
       if n >= 1475:
              raise Exception("n is too large"						)
       lowercase__:							Any         =     sqrt(5						)
       lowercase__:							Optional[int]         =     (1 + sqrt_a) / 2
       return [round(phi**i / sqrt_a						) for i in range(n + 1						)]


if __name__ == "__main__":
  UpperCamelCase					=     30
  time_func(fib_iterative, num)
  time_func(fib_recursive, num)  # Around 3s runtime
  time_func(fib_recursive_cached, num)  # Around 0ms runtime
  time_func(fib_memoization, num)
  time_func(fib_binet, num)







"""simple docstring"""







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

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					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=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

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

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

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

     # Patch sys.argv
     __A					:					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()



