
import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def      lowerCamelCase__       (       __lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			FileLock(str(tmpdir / "foo.lock"       )       )
			lowerCAmelCase_      			=			FileLock(str(tmpdir / "foo.lock"       )       )
			lowerCAmelCase_      			=			0.01
			with locka.acquire():
						with pytest.raises(__lowerCAmelCase       ):
									lowerCAmelCase_      			=			time.time()
									locka.acquire(__lowerCAmelCase       )
									assert time.time() - _start > timeout
def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"a" * 1000 + ".lock"
			lowerCAmelCase_      			=			FileLock(str(tmpdir / filename       )       )
			assert locka._lock_file.endswith(".lock"       )
			assert not locka._lock_file.endswith(__lowerCAmelCase       )
			assert len(os.path.basename(locka._lock_file       )       ) <= 255
			lowerCAmelCase_      			=			FileLock(tmpdir / filename       )
			with locka.acquire():
						with pytest.raises(__lowerCAmelCase       ):
									locka.acquire(0       )





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__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


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



