



'''simple docstring'''
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCamelCase									=      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
_UpperCamelCase					,	_UpperCamelCase					,	_UpperCamelCase					,	_UpperCamelCase									=      train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

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

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

_UpperCamelCase									=      knn.predict(X_new)

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









'''simple docstring'''
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment



class _A	(     unittest.TestCase     ):





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  inspect.getfile(accelerate.test_utils					)
					__UpperCAmelCase :   int    					=  os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_sync.py"""]					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main			,							num_processes=1					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main					)





		@require_single_gpu
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					test_sync.main()





		@require_multi_gpu
		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					print(f'Found {torch.cuda.device_count()} devices.'					)
					__UpperCAmelCase :   List[str]    					=  ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
					with patch_environment(omp_num_threads=1					):
								execute_subprocess_async(__UpperCAmelCase			,							env=os.environ.copy()					)




