
def 						UpperCAmelCase      (     a_ = "The quick brown fox jumps over the lazy dog"		,       )		->     bool:







				"""simple docstring"""



				__A             =			set()

				# Replace all the whitespace in our sentence
				__A             =			input_str.replace(" "		,       ""						)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()						)
				return len(a_						) == 2_6


def 						UpperCAmelCase      (     a_ = "The quick brown fox jumps over the lazy dog"		,       )		->     bool:







				"""simple docstring"""



				__A             =			[False] * 2_6
				for char in input_str:
								if char.islower():
												__A             =			True
								elif char.isupper():
												__A             =			True
				return all(a_						)


def 						UpperCAmelCase      (     a_ = "The quick brown fox jumps over the lazy dog"		,       )		->     bool:







				"""simple docstring"""



				return len({char for char in input_str.lower() if char.isalpha()}						) == 2_6


def 						UpperCAmelCase      (     )		->     None:







				"""simple docstring"""



				from timeit import timeit

				__A             =			"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
				print(timeit("is_pangram()"		,       setup=a_						)						)
				print(timeit("is_pangram_faster()"		,       setup=a_						)						)
				print(timeit("is_pangram_fastest()"		,       setup=a_						)						)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()


from collections import Counter

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

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



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