
"""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__    (       )			->   Optional[Any]:






    """simple docstring"""




    UpperCAmelCase          =       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      lowercase__    (       )			->   Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase          =       parse_args()

    # Import training_script as a module.
    UpperCAmelCase          =       Path(args.training_script			)
    sys.path.append(str(script_fpath.parent.resolve()			)			)
    UpperCAmelCase          =       script_fpath.stem
    UpperCAmelCase          =       importlib.import_module(lowerCAmelCase			)

    # Patch sys.argv
    UpperCAmelCase          =       [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"""

from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



