

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

import torch_xla.distributed.xla_multiprocessing as xmp
def  __SCREAMING_SNAKE_CASE       (				)       ->  int:
						__magic_name__				:							str				=      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=lowercase__      ,						default=1      ,						help='Number of TPU cores to use (1 or 8).'				)

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

						return parser.parse_args()
def  __SCREAMING_SNAKE_CASE       (				)       ->  List[str]:
						__magic_name__				:							List[Any]				=      parse_args()

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

						# Patch sys.argv
						__magic_name__				:							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 sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


