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

import torch_xla.distributed.xla_multiprocessing as xmp

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Union[str, 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=__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 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Any     			=						parse_args()

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

    # Patch sys.argv
    snake_case_ :    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()






'''simple docstring'''
import doctest
from collections import deque

import numpy as np






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    List[Any]     			=						[2, 1, 2, -1]
     snake_case_ :    int     			=						[1, 2, 3, 4]





 def        snake_case__		(    self):
     snake_case_ :    str     			=						len(self.first_signal)
     snake_case_ :    Any     			=						len(self.second_signal)

     snake_case_ :    List[Any]     			=						max(lowercase_ ,       lowercase_)

     # create a zero matrix of max_length x max_length
     snake_case_ :    Dict     			=						[[0] * max_length for i in range(lowercase_)]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(lowercase_):
         snake_case_ :    List[str]     			=						deque(self.second_signal)
         rotated_signal.rotate(lowercase_)
         for j, item in enumerate(lowercase_):
             matrix[i][j] += item

        # multiply the matrix with the first signal
     snake_case_ :    Any     			=						np.matmul(np.transpose(lowercase_) ,       np.transpose(self.first_signal))

     # rounding-off to two decimal places
     return [round(lowercase_ ,       2) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()




