

from collections import Counter

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

a_						     =    datasets.load_iris()

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

a_     ,	a_     ,	a_     ,	a_						     =    train_test_split(X, y)

def  lowerCamelCase__ (					_a							,			_a):
	return np.linalg.norm(np.array(_a) - np.array(_a))

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a=5):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       zip(_a							,			_a)
	# List of distances of all points from the point to be classified
	SCREAMING_SNAKE_CASE   :      str				     =       []
	for data_point in data:
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       euclidean_distance(data_point[0]							,			_a)
		distances.append((distance, data_point[1]))
	# Choosing 'k' points with the least distances.
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [i[1] for i in sorted(_a)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       Counter(_a).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]))


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(__A   )							,	'Tatoeba directory does not exist.'   )
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tempfile.mkdtemp()
				return TatoebaConverter(save_dir=a	)
			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				self.resolver.convert_models(["heb-eng"]	)






			@slow
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.resolver.write_model_card("opus-mt-he-en"      ,      dry_run=a	)
				assert mmeta["long_pair"] == "heb-eng"