




from collections import Counter

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

UpperCamelCase__    			=			datasets.load_iris()

UpperCamelCase__    			=			np.array(data["""data"""])
UpperCamelCase__    			=			np.array(data["""target"""])
UpperCamelCase__    			=			data["""target_names"""]

UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__    			=			train_test_split(X, y)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					return np.linalg.norm(np.array(SCREAMING_SNAKE_CASE_     ) - np.array(SCREAMING_SNAKE_CASE_     )     )







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










from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    			=			{"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					UpperCamelCase__    			=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




