{"id":343818,"date":"2023-01-11T21:00:40","date_gmt":"2023-01-11T21:00:40","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=343818"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=343818","title":{"rendered":"\u041a\u0430\u043a ChatGPT \u0437\u0430 \u043c\u0435\u043d\u044f \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u0435 \u0434\u043b\u044f \u0441\u043e\u0431\u0435\u0441\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u043f\u0438\u0441\u0430\u043b<\/span>"},"content":{"rendered":"
<\/div>\n
\n
\n
\n
\n

\u042f \u0441\u0435\u0439\u0447\u0430\u0441 \u0430\u043a\u0442\u0438\u0432\u043d\u043e \u0438\u0449\u0443 \u0440\u0430\u0431\u043e\u0442\u0443, \u043f\u0440\u043e\u0445\u043e\u0436\u0443 \u0441\u043e\u0431\u0435\u0441\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0438 \u0438\u043d\u043e\u0433\u0434\u0430 \u0440\u0430\u0431\u043e\u0442\u043e\u0434\u0430\u0442\u0435\u043b\u0438 \u043f\u0440\u0438\u0441\u044b\u043b\u0430\u044e\u0442 \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u044f, \u043f\u0440\u0435\u0436\u0434\u0435 \u0447\u0435\u043c \u043f\u0440\u0438\u0433\u043b\u0430\u0441\u0438\u0442\u044c \u043d\u0430 \u0438\u043d\u0442\u0435\u0440\u0432\u044c\u044e. \u0412 \u043e\u0447\u0435\u0440\u0435\u0434\u043d\u043e\u0439 \u0440\u0430\u0437 \u043c\u043d\u0435 \u043f\u0440\u0438\u0441\u043b\u0430\u043b\u0438 \u043f\u0440\u043e\u0441\u0442\u0435\u043d\u044c\u043a\u043e\u0435 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u0435, \u0432 \u0446\u0435\u043b\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u0442\u0430\u043a\u0438\u0445 \u043f\u043e\u0445\u043e\u0436\u0438\u0445: \u0438\u0437 \u0441\u043f\u0438\u0441\u043a\u0430 \u0432\u0435\u0440\u0448\u0438\u043d \u043f\u043e\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0434\u0435\u0440\u0435\u0432\u043e. <\/p>\n

\n\u0422\u0435\u0441\u0442\u043e\u0432\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u0435<\/summary>\n
\n

Write a python function that builds a tree based on a list of tuples of id (parent id, offspring id),
where None is the id of the root node.<\/p>\n

How this should work:<\/p>\n

source = [
(None, 'a'),
(None, 'b'),
(None, 'c'),
('a', 'a1'),
('a', 'a2'),
('a2', 'a21'),
('a2', 'a22'),
('b', 'b1'),
('b1', 'b11'),
('b11', 'b111'),
('b', 'b2'),
('c', 'c1'),
]<\/code><\/p>\n

expected = {
'a': {'a1': {}, 'a2': {'a21': {}, 'a22': {}}},
'b': {'b1': {'b11': {'b111': {}}}, 'b2': {}},
'c': {'c1': {}},
}<\/code><\/p>\n

assert to_tree(source) == expected<\/code><\/p>\n<\/p>\n<\/div>\n<\/details>\n

\u0418 \u043c\u043d\u0435 \u0432\u0434\u0440\u0443\u0433 \u043f\u043e\u0434\u0443\u043c\u0430\u043b\u043e\u0441\u044c, \u0430 \u043a\u0430\u043a \u0445\u0430\u0439\u043f\u043e\u0432\u044b\u0439 chatGPT \u043c\u043e\u0433 \u0431\u044b \u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c\u0441\u044f \u0441 \u0442\u0430\u043a\u0438\u043c \u043f\u043e\u043b\u0443\u0440\u0435\u0430\u043b\u044c\u043d\u044b\u043c, \u043d\u043e \u0432\u043f\u043e\u043b\u043d\u0435 \u043f\u043e\u043b\u0435\u0437\u043d\u044b\u043c \u0434\u043b\u044f \u043c\u0435\u043d\u044f, \u0437\u0430\u0434\u0430\u043d\u0438\u0435\u043c? \u041c\u044b\u0441\u043b\u044c \u043f\u0440\u0438\u0448\u043b\u0430, \u0430 \u0440\u0443\u043a\u0438 \u0443\u0436\u0435 \u0438\u0441\u043a\u0430\u043b\u0438 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e, \u043a\u0430\u043a \u043f\u0440\u043e\u0440\u0432\u0430\u0442\u044c\u0441\u044f \u043a chatGPT \u0438\u0437 \u0420\u043e\u0441\u0441\u0438\u0438. \u0421\u043f\u0443\u0441\u0442\u044f \u043a\u0430\u043a\u043e\u0435-\u0442\u043e \u0432\u0440\u0435\u043c\u044f \u043f\u0440\u043e\u0440\u0432\u0430\u043b\u0441\u044f \u0438 \u043f\u0440\u044f\u043c \u0432 \u043b\u043e\u0431, \u043d\u0438\u0447\u0435\u0433\u043e \u043d\u0435 \u043c\u0435\u043d\u044f\u044f \u0432 \u0442\u0435\u043a\u0441\u0442\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u044f, \u043e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u044e \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0435 \u0438 \u0441 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u043c \u0436\u0434\u0443. <\/p>\n

\u0414\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0431\u044b\u0441\u0442\u0440\u043e chatGPT \u043f\u0438\u0448\u0435\u0442 \u043c\u043d\u0435 \u043e\u0442\u0432\u0435\u0442 \u0441\u043e \u0441\u0432\u043e\u0438\u043c \u0440\u0435\u0448\u0435\u043d\u0438\u0435\u043c \u0438 \u0434\u0430\u0436\u0435 \u0447\u0442\u043e-\u0442\u043e \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442.<\/p>\n

\n\u0421\u0430\u043c\u043e\u0435 \u043f\u0435\u0440\u0432\u043e\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435<\/summary>\n
\n
def to_tree(source):     tree = {}     for parent, offspring in source:         if parent is None:             tree[offspring] = {}         else:             tree[parent][offspring] = {}     for parent, offspring in source:         if parent is not None:             tree[parent][offspring] = to_tree(                 [(p, o) for p, o in source if p == offspring]             )     return tree<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u0412\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u043d\u0430 \u043f\u0435\u0440\u0432\u044b\u0439 \u043f\u043e\u0432\u0435\u0440\u0445\u043d\u043e\u0441\u0442\u043d\u044b\u0439 \u0432\u0437\u0433\u043b\u044f\u0434 \u043f\u0440\u0430\u0432\u0434\u043e\u043f\u043e\u0434\u043e\u0431\u043d\u043e, \u0441\u043c\u0443\u0449\u0430\u044e\u0442 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u043c\u0435\u043d\u0442\u044b, \u043d\u043e \u044d\u0442\u043e \u0432\u043f\u043e\u043b\u043d\u0435 \u043a\u043e\u0434. \u0417\u0430\u043f\u0443\u0441\u043a\u0430\u044e \u044d\u0442\u043e\u0442 \u043a\u043e\u0434 \u0432 Python \u0438 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u043e\u043d \u043d\u0435 \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442. \u0421 \u0447\u0443\u0432\u0441\u0442\u0432\u043e\u043c \u043f\u0440\u0435\u0432\u043e\u0441\u0445\u043e\u0434\u0441\u0442\u0432\u0430 \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u043d\u0430\u0434 \u0431\u0435\u0437\u0434\u0443\u0448\u043d\u043e\u0439 \u043c\u0430\u0448\u0438\u043d\u043e\u0439, \u043f\u044b\u0442\u0430\u044e\u0441\u044c \u0432\u044b\u0434\u0430\u0432\u0438\u0442\u044c \u0435\u0449\u0435 \u0447\u0442\u043e-\u0442\u043e \u0438\u0437 chatGPT \u0438 \u043f\u0440\u043e\u0448\u0443 \u043f\u0440\u043e\u0430\u043d\u043d\u043e\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0442\u0438\u043f\u044b. \u041e\u043d \u0431\u043e\u043b\u0435\u0435-\u043c\u0435\u043d\u0435\u0435 \u0441\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442\u0441\u044f. \u041f\u0440\u043e\u0448\u0443 \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u044c. \u0412 \u0446\u0435\u043b\u043e\u043c, \u043e\u043d \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u043e\u0446\u0435\u043d\u0438\u0432\u0430\u0435\u0442, \u043a\u0430\u043a O(n*n)  \u0438 \u0434\u0430\u0436\u0435 \u0434\u0430\u0435\u0442 \u0441\u043e\u0432\u0435\u0442\u044b, \u043a\u0430\u043a \u0443\u043b\u0443\u0447\u0448\u0438\u0442\u044c — \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439, \u0433\u043e\u0432\u043e\u0440\u0438\u0442, \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445. <\/p>\n
\u042f \u0442\u043e\u0436\u0435 \u043d\u0435 \u043f\u0440\u043e\u043c\u0430\u0445, \u0438 \u043f\u0440\u043e\u0448\u0443 \u0435\u0433\u043e \u0441\u0430\u043c\u043e\u0433\u043e \u0443\u043b\u0443\u0447\u0448\u0438\u0442\u044c \u043a\u043e\u0434 \u0441 \u0442\u043e\u0447\u043a\u0438 \u0437\u0440\u0435\u043d\u0438\u044f \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u0438. \u0418 \u044d\u0442\u043e \u0447\u0443\u0434\u043e \u0432\u044b\u0434\u0430\u0435\u0442 \u043c\u043d\u0435 \u0447\u0442\u043e-\u0442\u043e \u043e\u0447\u0435\u043d\u044c \u0441\u0442\u0440\u0430\u043d\u043d\u043e\u0435.<\/p>\n
\n\u041f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u0431\u0435\u0441\u0441\u043c\u044b\u0441\u043b\u0435\u043d\u043d\u044b\u0439 \u043a\u043e\u0434<\/summary>\n
\n
def to_tree(source: List[Tuple[str, str]]) -> Dict[str, str]:     # Initialize an empty tree     tree = {}          # Iterate through the source list and add the nodes to the tree     for parent, offspring in source:         tree[offspring] = parent          # Return the tree     return tree<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u041f\u043e\u0441\u043b\u0435 \u044d\u0442\u043e\u0433\u043e \u0441\u043f\u0435\u0448\u0443 \u043a \u0434\u0440\u0443\u0433\u0443 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0443, \u0447\u0442\u043e\u0431\u044b \u0437\u0430 \u0433\u043b\u0430\u0437\u0430 \u043f\u0435\u0441\u043e\u0447\u0438\u0442\u044c chatGPT \u0437\u0430 \u043a\u0440\u0438\u0432\u043e\u0440\u0443\u043a\u043e\u0441\u0442\u044c \u0438 \u0442\u0443\u043f\u043e\u0441\u0442\u044c. \u0412 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0435 \u0441\u043d\u0438\u0441\u0445\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u043e\u0431\u0441\u0443\u0436\u0434\u0435\u043d\u0438\u044f \u043c\u044b \u043f\u0440\u0438\u0445\u043e\u0434\u0438\u043c \u043a \u043c\u044b\u0441\u043b\u0438, \u0447\u0442\u043e \u0432\u043e\u043e\u0431\u0449\u0435-\u0442\u043e \u0440\u0430\u0431\u043e\u0442\u0430 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0430 \u043d\u0430 \u043f\u043e\u043b\u043e\u0432\u0438\u043d\u0443 \u0441\u043e\u0441\u0442\u043e\u0438\u0442 \u0438\u0437 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u043f\u043e\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0438 \u0437\u0430\u0434\u0430\u0447\u0438. \u0421 \u044d\u0442\u043e\u0439 \u043c\u044b\u0441\u043b\u044c\u044e \u0438 \u0441 \u0447\u0443\u0442\u044c \u0443\u0433\u0430\u0441\u0448\u0438\u043c \u0447\u0443\u0432\u0441\u0442\u0432\u043e\u043c \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0432\u043e\u0441\u0445\u043e\u0434\u0441\u0442\u0432\u0430, \u044f \u0440\u0435\u0448\u0438\u043b \u0447\u0443\u0442\u044c \u043f\u043e\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u0440\u0435\u0448\u0435\u043d\u0438\u0435 chatGPT \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0435\u0433\u043e \u0436\u0435 \u0441\u043e\u0432\u0435\u0442 \u043f\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044e \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0445 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n
\n\u041c\u043e\u0438 \u043f\u0440\u0430\u0432\u043a\u0438<\/summary>\n
\n
from typing import List, Tuple, Dict, Optional, Any   def to_tree(data: List[Tuple[Optional[str], str]]) -> Dict[str, Any]:     children: Dict[str, Any] = {o: {} for _, o in data}     tree: Dict[str, Any] = {}     for parent, offspring in data:         if parent is None:             tree[offspring] = children[offspring]         else:             children[parent][offspring] = children[offspring]     return tree<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u0418 \u043e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u044e \u0435\u043c\u0443 \u044d\u0442\u043e\u0442 \u043a\u043e\u0434. \u041d\u0430 \u0447\u0442\u043e chatGPT \u0437\u0430\u043c\u0435\u0447\u0430\u0435\u0442, \u0447\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u043b\u0443\u0447\u0448\u0435 \u0438 \u0447\u0443\u0442\u044c \u043b\u0438 \u043d\u0435 \u0445\u0432\u0430\u043b\u0438\u0442 \u043c\u0435\u043d\u044f. \u0421\u043f\u0430\u0441\u0438\u0431\u043e<\/em>, \u043a\u043e\u043d\u0435\u0447\u043d\u043e, chatGPT, \u043d\u043e \u0434\u0430\u0432\u0430\u0439 \u0443\u0436 \u0442\u043e\u0433\u0434\u0430 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0434\u0430\u043b\u044c\u0448\u0435. \u041f\u043e\u0441\u043b\u0435 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u044d\u0442\u043e\u0442 \u0441\u043b\u043e\u0436\u043d\u044b\u0439 \u0434\u043b\u044f \u0437\u0430\u0434\u0430\u0447\u0438 \u043c\u043e\u043c\u0435\u043d\u0442 \u043c\u044b \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u044b\u043c\u0438 \u0443\u0441\u0438\u043b\u0438\u044f\u043c\u0438 \u0440\u0435\u0448\u0438\u043b\u0438, \u0430 \u043c\u043e\u044f \u0441\u043f\u0435\u0441\u044c \u0443\u0448\u043b\u0430, \u0440\u0430\u0431\u043e\u0442\u0430 \u043f\u043e\u0448\u043b\u0430 \u0433\u043e\u0440\u0430\u0437\u0434\u043e \u043b\u0435\u0433\u0447\u0435. <\/p>\n
\u041f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0442\u0435\u0441\u0442\u044b — \u043e\u043d \u043d\u0430\u043f\u0438\u0441\u0430\u043b \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043d\u0435\u043f\u043b\u043e\u0445\u0438\u0445. \u041f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0442\u0435\u0441\u0442\u044b \u0441 \u043d\u0435\u0444\u043e\u0440\u043c\u0430\u0442\u043d\u044b\u043c\u0438 \u0432\u0445\u043e\u0434\u043d\u044b\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 — \u0442\u043e\u0436\u0435 \u043d\u0430\u043f\u0440\u0438\u0434\u0443\u043c\u044b\u0432\u0430\u043b \u043a\u043e\u0435-\u0447\u0442\u043e. \u041f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u043e\u0431\u043d\u043e\u0432\u0438\u0442\u044c \u0440\u0435\u0448\u0435\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u0442\u0435\u0441\u0442\u044b \u0441 \u043d\u0435\u0444\u043e\u0440\u043c\u0430\u0442\u043d\u044b\u043c\u0438 \u0432\u0445\u043e\u0434\u043d\u044b\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u043f\u0440\u043e\u0445\u043e\u0434\u0438\u043b\u0438 — \u043e\u043d \u0441\u043d\u043e\u0432\u0430 \u0434\u043e\u043f\u0438\u0441\u0430\u043b \u0431\u043b\u043e\u043a\u0438 \u043a\u043e\u0434\u0430. \u0422\u043e \u0436\u0435 \u0441\u0430\u043c\u043e\u0435 \u043c\u044b \u0441 \u043d\u0438\u043c \u043f\u0440\u043e\u0432\u0435\u0440\u043d\u0443\u043b\u0438 \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u043f\u043e\u0433\u0440\u0430\u043d\u0438\u0447\u043d\u044b\u043c\u0438 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u043c\u0438. \u041a\u043e\u043d\u0435\u0447\u043d\u043e, \u0447\u0442\u043e-\u0442\u043e \u043f\u0440\u0438\u0445\u043e\u0434\u0438\u043b\u043e\u0441\u044c \u0435\u043c\u0443 \u043f\u043e\u0434\u043f\u0440\u0430\u0432\u043b\u044f\u0442\u044c \u043f\u043e-\u0434\u0440\u0443\u0436\u0435\u0441\u043a\u0438. \u041c\u044b \u0436 \u0434\u0440\u0443\u0437\u044c\u044f \u0441\u0442\u0430\u043b\u0438. \u041d\u0443 \u0438 \u0432 \u043a\u043e\u043d\u0446\u0435 \u043f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u0435\u0433\u043e \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u043e \u043e\u043f\u0438\u0441\u0430\u0442\u044c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043f\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0438 \u043f\u043e \u043f\u0430\u043c\u044f\u0442\u0438, \u0441 \u0447\u0435\u043c \u043e\u043d \u0442\u043e\u0436\u0435 \u0441\u043f\u0440\u0430\u0432\u0438\u043b\u0441\u044f. <\/p>\n
\n\u041a\u043e\u043d\u0435\u0447\u043d\u044b\u0439 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0439 \u043a\u043e\u0434<\/summary>\n
\n
from typing import List, Tuple, Dict, Optional, Any   # Time complexity: # The time complexity of this function is O(n), where n is the number of tuples in the input list. The reason is that # the function iterates over the input list once and performs a constant number of # operations (a constant number of type and value comparisons) on each element of the list. Therefore, # the total number of operations is directly proportional to the number of elements in the list, which makes # the time complexity O(n). # # Space complexity: # The space complexity of this function is O(n). The reason is that the function creates two data structures: tree and # children both with the same size as the number of tuples in the input list, to store the tree and # children of the each node. Therefore, the space complexity is directly proportional to the number of # elements in the input list, which makes the space complexity O(n). def to_tree(data: List[Tuple[Optional[str], str]]) -> Dict[str, Any]:     if not data:         return {}     if not isinstance(data, list):         raise ValueError(\"Input data must be a list\")     if not all(map(lambda x: isinstance(x, tuple), data)):         raise ValueError(\"Input data must be a list of tuples\")     if not all(map(lambda x: len(x) == 2, data)):         raise ValueError(\"Each tuple in input data must have exactly two elements\")     if not all(map(lambda x: (isinstance(x[0], str) or x[0] is None) and isinstance(x[1], str), data)):         raise ValueError(\"Parent and offspring must be of type str, parent can be None\")     check_offspring_duplicates(data)      children: Dict[str, Any] = {o: {} for _, o in data}     tree: Dict[str, Any] = {}     for parent, offspring in data:         if parent is None:             tree[offspring] = children[offspring]         else:             children[parent][offspring] = children[offspring]     return tree   # Time complexity of this function is O(n) as it also iterates over the input list once, and does a constant number of # dictionary operations on each iteration, where n is the number of tuples in the input list. # # Space complexity of this function is O(n) too, where n is the number of unique offsprings in the input data, because # it's creating a dictionary to store the count of each offspring and the size of the dictionary will be equal to the # number of unique offsprings in the input data. def check_offspring_duplicates(data: List[Tuple[Optional[str], str]]):     offspring_count = {}     for _, offspring in data:         if offspring in offspring_count:             offspring_count[offspring] += 1             raise ValueError(f\"Offspring {offspring} appears more than once in the input data\")         else:             offspring_count[offspring] = 1   # Tests  def test_to_tree():     source = [         (None, 'a'),         (None, 'b'),         (None, 'c'),         ('a', 'a1'),         ('a', 'a2'),         ('a2', 'a21'),         ('a2', 'a22'),         ('b', 'b1'),         ('b1', 'b11'),         ('b11', 'b111'),         ('b', 'b2'),         ('c', 'c1'),     ]      expected = {         'a': {'a1': {}, 'a2': {'a21': {}, 'a22': {}}},         'b': {'b1': {'b11': {'b111': {}}}, 'b2': {}},         'c': {'c1': {}},     }      assert to_tree(source) == expected   def test_to_tree_with_invalid_inputs():     # Test with empty input     assert to_tree([]) == {}      # Test with non-tuple input     try:         to_tree(['a', 'b'])     except ValueError as v:         assert str(v) == \"Input data must be a list of tuples\"     else:         assert False      # Test with tuple input with wrong number of elements     try:         to_tree([(None,), ('a', 'b', 'c')])     except ValueError as v:         assert str(v) == \"Each tuple in input data must have exactly two elements\"     else:         assert False   def test_input_type():     # Test input with non-string parent and offspring     try:         to_tree([(None, 1), (1, 2)])     except ValueError as v:         assert str(v) == \"Parent and offspring must be of type str, parent can be None\"     else:         assert False      try:         to_tree([(None, '1'), ('1', 2)])     except ValueError as v:         assert str(v) == \"Parent and offspring must be of type str, parent can be None\"     else:         assert False      try:         to_tree([(None, None)])     except ValueError as v:         assert str(v) == \"Parent and offspring must be of type str, parent can be None\"     else:         assert False      # Test input with string parent and offspring     assert to_tree([(None, '1'), ('1', '2')]) == {'1': {'2': {}}}   def test_duplicate_nodes():      try:         to_tree([(None, '1'), ('1', '2'), ('1', '2')])     except ValueError as v:         assert str(v) == \"Offspring 2 appears more than once in the input data\"     else:         assert False      try:         to_tree([(None, '1'), ('1', '2'), ('2', '2')])     except ValueError as v:         assert str(v) == \"Offspring 2 appears more than once in the input data\"     else:         assert False   test_to_tree() test_input_type() test_to_tree_with_invalid_inputs() test_duplicate_nodes()   <\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0448\u0442\u0440\u0438\u0445\u0438, \u043d\u043e \u0442\u0443\u0442 chatGPT \u0443\u0441\u0442\u0430\u043b — \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432 \u0432 \u0447\u0430\u0441, \u0442\u0430\u043a \u0438 \u0441\u043a\u0430\u0437\u0430\u043b<\/em>. <\/p>\n
\u0427\u0442\u043e \u0436, \u0432 \u0442\u0430\u043a\u043e\u043c \u0432\u0438\u0434\u0435 \u044f \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u0438\u043b \u0440\u0430\u0431\u043e\u0442\u043e\u0434\u0430\u0442\u0435\u043b\u044e. \u0416\u0434\u0443 \u0442\u0435\u043f\u0435\u0440\u044c \u043e\u0442\u0432\u0435\u0442\u0430.<\/p>\n
\u0412 \u0438\u0442\u043e\u0433\u0435 \u043e\u043f\u044b\u0442 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u0441\u044f \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u044b\u043c \u0438, \u043a\u0430\u043a \u043c\u043d\u0435 \u043a\u0430\u0436\u0435\u0442\u0441\u044f, chatGPT \u043c\u043e\u0436\u0435\u0442 \u0441\u0442\u0430\u0442\u044c \u0445\u043e\u0440\u043e\u0448\u0438\u043c \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u043e\u043c \u0432 \u043f\u043e\u043c\u043e\u0449\u044c \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0443, \u043e\u043d \u043c\u043e\u0436\u0435\u0442 \u0431\u0440\u0430\u0442\u044c \u043d\u0430 \u0441\u0435\u0431\u044f \u043a\u0430\u043a\u0438\u0435-\u0442\u043e \u0442\u0440\u0438\u0432\u0438\u0430\u043b\u044c\u043d\u044b\u0435 \u0438 \u0440\u0443\u0442\u0438\u043d\u043d\u044b\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u043d\u0430 \u0441\u0432\u043e\u0435\u043c \u0443\u0440\u043e\u0432\u043d\u0435 \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0446\u0438\u0438, \u0430 \u0441\u0430\u043c\u043e\u043c\u0443 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0443 \u043f\u0440\u0438\u0434\u0435\u0442\u0441\u044f \u0443\u0447\u0438\u0442\u044c\u0441\u044f \u0447\u0435\u0442\u0447\u0435 \u0441\u0442\u0430\u0432\u0438\u0442\u044c \u0437\u0430\u0434\u0430\u0447\u0438, \u0440\u0435\u0432\u044c\u044e\u0438\u0442\u044c \u043a\u043e\u0434 \u0438 \u0432\u044b\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0442\u044c \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0443. <\/p>\n<\/div>\n<\/div>\n<\/div>\n
  <\/div>\n
  
 \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438  https:\/\/habr.com\/ru\/post\/710169\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
<\/div>\n
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\u042f \u0441\u0435\u0439\u0447\u0430\u0441 \u0430\u043a\u0442\u0438\u0432\u043d\u043e \u0438\u0449\u0443 \u0440\u0430\u0431\u043e\u0442\u0443, \u043f\u0440\u043e\u0445\u043e\u0436\u0443 \u0441\u043e\u0431\u0435\u0441\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0438 \u0438\u043d\u043e\u0433\u0434\u0430 \u0440\u0430\u0431\u043e\u0442\u043e\u0434\u0430\u0442\u0435\u043b\u0438 \u043f\u0440\u0438\u0441\u044b\u043b\u0430\u044e\u0442 \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u044f, \u043f\u0440\u0435\u0436\u0434\u0435 \u0447\u0435\u043c \u043f\u0440\u0438\u0433\u043b\u0430\u0441\u0438\u0442\u044c \u043d\u0430 \u0438\u043d\u0442\u0435\u0440\u0432\u044c\u044e. \u0412 \u043e\u0447\u0435\u0440\u0435\u0434\u043d\u043e\u0439 \u0440\u0430\u0437 \u043c\u043d\u0435 \u043f\u0440\u0438\u0441\u043b\u0430\u043b\u0438  \u043f\u0440\u043e\u0441\u0442\u0435\u043d\u044c\u043a\u043e\u0435 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u0435, \u0432 \u0446\u0435\u043b\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u0442\u0430\u043a\u0438\u0445 \u043f\u043e\u0445\u043e\u0436\u0438\u0445: \u0438\u0437 \u0441\u043f\u0438\u0441\u043a\u0430 \u0432\u0435\u0440\u0448\u0438\u043d \u043f\u043e\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0434\u0435\u0440\u0435\u0432\u043e. <\/p>\n
\n\u0422\u0435\u0441\u0442\u043e\u0432\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u0435<\/summary>\n
\n
Write a python function that builds a tree based on a list of tuples of id (parent id, offspring id),
 where None is the id of the root node.<\/p>\n
How this should work:<\/p>\n
source = [
 (None, 'a'),
 (None, 'b'),
 (None, 'c'),
 ('a', 'a1'),
 ('a', 'a2'),
 ('a2', 'a21'),
 ('a2', 'a22'),
 ('b', 'b1'),
 ('b1', 'b11'),
 ('b11', 'b111'),
 ('b', 'b2'),
 ('c', 'c1'),
 ]<\/code><\/p>\n
expected = {
 'a': {'a1': {}, 'a2': {'a21': {}, 'a22': {}}},
 'b': {'b1': {'b11': {'b111': {}}}, 'b2': {}},
 'c': {'c1': {}},
 }<\/code><\/p>\n
assert to_tree(source) == expected<\/code><\/p>\n<\/p>\n<\/div>\n<\/details>\n
\u0418 \u043c\u043d\u0435 \u0432\u0434\u0440\u0443\u0433 \u043f\u043e\u0434\u0443\u043c\u0430\u043b\u043e\u0441\u044c, \u0430 \u043a\u0430\u043a \u0445\u0430\u0439\u043f\u043e\u0432\u044b\u0439 chatGPT \u043c\u043e\u0433 \u0431\u044b \u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c\u0441\u044f \u0441 \u0442\u0430\u043a\u0438\u043c \u043f\u043e\u043b\u0443\u0440\u0435\u0430\u043b\u044c\u043d\u044b\u043c, \u043d\u043e \u0432\u043f\u043e\u043b\u043d\u0435 \u043f\u043e\u043b\u0435\u0437\u043d\u044b\u043c \u0434\u043b\u044f \u043c\u0435\u043d\u044f, \u0437\u0430\u0434\u0430\u043d\u0438\u0435\u043c? \u041c\u044b\u0441\u043b\u044c \u043f\u0440\u0438\u0448\u043b\u0430, \u0430 \u0440\u0443\u043a\u0438 \u0443\u0436\u0435 \u0438\u0441\u043a\u0430\u043b\u0438 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e, \u043a\u0430\u043a \u043f\u0440\u043e\u0440\u0432\u0430\u0442\u044c\u0441\u044f \u043a chatGPT \u0438\u0437 \u0420\u043e\u0441\u0441\u0438\u0438. \u0421\u043f\u0443\u0441\u0442\u044f \u043a\u0430\u043a\u043e\u0435-\u0442\u043e \u0432\u0440\u0435\u043c\u044f \u043f\u0440\u043e\u0440\u0432\u0430\u043b\u0441\u044f \u0438 \u043f\u0440\u044f\u043c \u0432 \u043b\u043e\u0431, \u043d\u0438\u0447\u0435\u0433\u043e \u043d\u0435 \u043c\u0435\u043d\u044f\u044f \u0432 \u0442\u0435\u043a\u0441\u0442\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u044f, \u043e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u044e \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0435 \u0438 \u0441 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u043c \u0436\u0434\u0443. <\/p>\n
\u0414\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0431\u044b\u0441\u0442\u0440\u043e chatGPT \u043f\u0438\u0448\u0435\u0442 \u043c\u043d\u0435 \u043e\u0442\u0432\u0435\u0442 \u0441\u043e \u0441\u0432\u043e\u0438\u043c \u0440\u0435\u0448\u0435\u043d\u0438\u0435\u043c \u0438 \u0434\u0430\u0436\u0435 \u0447\u0442\u043e-\u0442\u043e \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442.<\/p>\n
\n\u0421\u0430\u043c\u043e\u0435 \u043f\u0435\u0440\u0432\u043e\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435<\/summary>\n
\n
def to_tree(source):     tree = {}     for parent, offspring in source:         if parent is None:             tree[offspring] = {}         else:             tree[parent][offspring] = {}     for parent, offspring in source:         if parent is not None:             tree[parent][offspring] = to_tree(                 [(p, o) for p, o in source if p == offspring]             )     return tree<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u0412\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u043d\u0430 \u043f\u0435\u0440\u0432\u044b\u0439 \u043f\u043e\u0432\u0435\u0440\u0445\u043d\u043e\u0441\u0442\u043d\u044b\u0439 \u0432\u0437\u0433\u043b\u044f\u0434 \u043f\u0440\u0430\u0432\u0434\u043e\u043f\u043e\u0434\u043e\u0431\u043d\u043e, \u0441\u043c\u0443\u0449\u0430\u044e\u0442 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u043c\u0435\u043d\u0442\u044b, \u043d\u043e \u044d\u0442\u043e \u0432\u043f\u043e\u043b\u043d\u0435 \u043a\u043e\u0434. \u0417\u0430\u043f\u0443\u0441\u043a\u0430\u044e \u044d\u0442\u043e\u0442 \u043a\u043e\u0434 \u0432 Python \u0438 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u043e\u043d \u043d\u0435 \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442. \u0421 \u0447\u0443\u0432\u0441\u0442\u0432\u043e\u043c \u043f\u0440\u0435\u0432\u043e\u0441\u0445\u043e\u0434\u0441\u0442\u0432\u0430 \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u043d\u0430\u0434 \u0431\u0435\u0437\u0434\u0443\u0448\u043d\u043e\u0439 \u043c\u0430\u0448\u0438\u043d\u043e\u0439, \u043f\u044b\u0442\u0430\u044e\u0441\u044c \u0432\u044b\u0434\u0430\u0432\u0438\u0442\u044c \u0435\u0449\u0435 \u0447\u0442\u043e-\u0442\u043e \u0438\u0437 chatGPT \u0438 \u043f\u0440\u043e\u0448\u0443 \u043f\u0440\u043e\u0430\u043d\u043d\u043e\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0442\u0438\u043f\u044b. \u041e\u043d \u0431\u043e\u043b\u0435\u0435-\u043c\u0435\u043d\u0435\u0435 \u0441\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442\u0441\u044f. \u041f\u0440\u043e\u0448\u0443 \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u044c. \u0412 \u0446\u0435\u043b\u043e\u043c, \u043e\u043d \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u043e\u0446\u0435\u043d\u0438\u0432\u0430\u0435\u0442, \u043a\u0430\u043a O(n*n)  \u0438 \u0434\u0430\u0436\u0435 \u0434\u0430\u0435\u0442 \u0441\u043e\u0432\u0435\u0442\u044b, \u043a\u0430\u043a \u0443\u043b\u0443\u0447\u0448\u0438\u0442\u044c — \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439, \u0433\u043e\u0432\u043e\u0440\u0438\u0442, \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445. <\/p>\n
\u042f \u0442\u043e\u0436\u0435 \u043d\u0435 \u043f\u0440\u043e\u043c\u0430\u0445, \u0438 \u043f\u0440\u043e\u0448\u0443 \u0435\u0433\u043e \u0441\u0430\u043c\u043e\u0433\u043e \u0443\u043b\u0443\u0447\u0448\u0438\u0442\u044c \u043a\u043e\u0434 \u0441 \u0442\u043e\u0447\u043a\u0438 \u0437\u0440\u0435\u043d\u0438\u044f \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u0438. \u0418 \u044d\u0442\u043e \u0447\u0443\u0434\u043e \u0432\u044b\u0434\u0430\u0435\u0442 \u043c\u043d\u0435 \u0447\u0442\u043e-\u0442\u043e \u043e\u0447\u0435\u043d\u044c \u0441\u0442\u0440\u0430\u043d\u043d\u043e\u0435.<\/p>\n
\n\u041f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u0431\u0435\u0441\u0441\u043c\u044b\u0441\u043b\u0435\u043d\u043d\u044b\u0439 \u043a\u043e\u0434<\/summary>\n
\n
def to_tree(source: List[Tuple[str, str]]) -> Dict[str, str]:     # Initialize an empty tree     tree = {}          # Iterate through the source list and add the nodes to the tree     for parent, offspring in source:         tree[offspring] = parent          # Return the tree     return tree<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u041f\u043e\u0441\u043b\u0435 \u044d\u0442\u043e\u0433\u043e \u0441\u043f\u0435\u0448\u0443 \u043a \u0434\u0440\u0443\u0433\u0443 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0443, \u0447\u0442\u043e\u0431\u044b \u0437\u0430 \u0433\u043b\u0430\u0437\u0430 \u043f\u0435\u0441\u043e\u0447\u0438\u0442\u044c chatGPT \u0437\u0430 \u043a\u0440\u0438\u0432\u043e\u0440\u0443\u043a\u043e\u0441\u0442\u044c \u0438 \u0442\u0443\u043f\u043e\u0441\u0442\u044c. \u0412 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0435 \u0441\u043d\u0438\u0441\u0445\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u043e\u0431\u0441\u0443\u0436\u0434\u0435\u043d\u0438\u044f \u043c\u044b \u043f\u0440\u0438\u0445\u043e\u0434\u0438\u043c \u043a \u043c\u044b\u0441\u043b\u0438, \u0447\u0442\u043e \u0432\u043e\u043e\u0431\u0449\u0435-\u0442\u043e \u0440\u0430\u0431\u043e\u0442\u0430 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0430 \u043d\u0430 \u043f\u043e\u043b\u043e\u0432\u0438\u043d\u0443 \u0441\u043e\u0441\u0442\u043e\u0438\u0442 \u0438\u0437 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u043f\u043e\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0438 \u0437\u0430\u0434\u0430\u0447\u0438. \u0421 \u044d\u0442\u043e\u0439 \u043c\u044b\u0441\u043b\u044c\u044e \u0438 \u0441 \u0447\u0443\u0442\u044c \u0443\u0433\u0430\u0441\u0448\u0438\u043c \u0447\u0443\u0432\u0441\u0442\u0432\u043e\u043c \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0432\u043e\u0441\u0445\u043e\u0434\u0441\u0442\u0432\u0430, \u044f \u0440\u0435\u0448\u0438\u043b \u0447\u0443\u0442\u044c \u043f\u043e\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u0440\u0435\u0448\u0435\u043d\u0438\u0435 chatGPT \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0435\u0433\u043e \u0436\u0435 \u0441\u043e\u0432\u0435\u0442 \u043f\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044e \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0445 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n
\n\u041c\u043e\u0438 \u043f\u0440\u0430\u0432\u043a\u0438<\/summary>\n
\n
from typing import List, Tuple, Dict, Optional, Any   def to_tree(data: List[Tuple[Optional[str], str]]) -> Dict[str, Any]:     children: Dict[str, Any] = {o: {} for _, o in data}     tree: Dict[str, Any] = {}     for parent, offspring in data:         if parent is None:             tree[offspring] = children[offspring]         else:             children[parent][offspring] = children[offspring]     return tree<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u0418 \u043e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u044e \u0435\u043c\u0443 \u044d\u0442\u043e\u0442 \u043a\u043e\u0434. \u041d\u0430 \u0447\u0442\u043e chatGPT \u0437\u0430\u043c\u0435\u0447\u0430\u0435\u0442, \u0447\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u043b\u0443\u0447\u0448\u0435 \u0438 \u0447\u0443\u0442\u044c \u043b\u0438 \u043d\u0435 \u0445\u0432\u0430\u043b\u0438\u0442 \u043c\u0435\u043d\u044f. \u0421\u043f\u0430\u0441\u0438\u0431\u043e<\/em>, \u043a\u043e\u043d\u0435\u0447\u043d\u043e, chatGPT, \u043d\u043e \u0434\u0430\u0432\u0430\u0439 \u0443\u0436 \u0442\u043e\u0433\u0434\u0430 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0434\u0430\u043b\u044c\u0448\u0435. \u041f\u043e\u0441\u043b\u0435 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u044d\u0442\u043e\u0442 \u0441\u043b\u043e\u0436\u043d\u044b\u0439 \u0434\u043b\u044f \u0437\u0430\u0434\u0430\u0447\u0438 \u043c\u043e\u043c\u0435\u043d\u0442 \u043c\u044b \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u044b\u043c\u0438 \u0443\u0441\u0438\u043b\u0438\u044f\u043c\u0438 \u0440\u0435\u0448\u0438\u043b\u0438, \u0430 \u043c\u043e\u044f \u0441\u043f\u0435\u0441\u044c \u0443\u0448\u043b\u0430, \u0440\u0430\u0431\u043e\u0442\u0430 \u043f\u043e\u0448\u043b\u0430 \u0433\u043e\u0440\u0430\u0437\u0434\u043e \u043b\u0435\u0433\u0447\u0435. <\/p>\n
\u041f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0442\u0435\u0441\u0442\u044b — \u043e\u043d \u043d\u0430\u043f\u0438\u0441\u0430\u043b \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043d\u0435\u043f\u043b\u043e\u0445\u0438\u0445. \u041f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0442\u0435\u0441\u0442\u044b \u0441 \u043d\u0435\u0444\u043e\u0440\u043c\u0430\u0442\u043d\u044b\u043c\u0438 \u0432\u0445\u043e\u0434\u043d\u044b\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 — \u0442\u043e\u0436\u0435 \u043d\u0430\u043f\u0440\u0438\u0434\u0443\u043c\u044b\u0432\u0430\u043b \u043a\u043e\u0435-\u0447\u0442\u043e. \u041f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u043e\u0431\u043d\u043e\u0432\u0438\u0442\u044c \u0440\u0435\u0448\u0435\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u0442\u0435\u0441\u0442\u044b \u0441 \u043d\u0435\u0444\u043e\u0440\u043c\u0430\u0442\u043d\u044b\u043c\u0438 \u0432\u0445\u043e\u0434\u043d\u044b\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u043f\u0440\u043e\u0445\u043e\u0434\u0438\u043b\u0438 — \u043e\u043d \u0441\u043d\u043e\u0432\u0430 \u0434\u043e\u043f\u0438\u0441\u0430\u043b \u0431\u043b\u043e\u043a\u0438 \u043a\u043e\u0434\u0430. \u0422\u043e \u0436\u0435 \u0441\u0430\u043c\u043e\u0435 \u043c\u044b \u0441 \u043d\u0438\u043c \u043f\u0440\u043e\u0432\u0435\u0440\u043d\u0443\u043b\u0438 \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u043f\u043e\u0433\u0440\u0430\u043d\u0438\u0447\u043d\u044b\u043c\u0438 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u043c\u0438. \u041a\u043e\u043d\u0435\u0447\u043d\u043e, \u0447\u0442\u043e-\u0442\u043e \u043f\u0440\u0438\u0445\u043e\u0434\u0438\u043b\u043e\u0441\u044c \u0435\u043c\u0443 \u043f\u043e\u0434\u043f\u0440\u0430\u0432\u043b\u044f\u0442\u044c \u043f\u043e-\u0434\u0440\u0443\u0436\u0435\u0441\u043a\u0438. \u041c\u044b \u0436 \u0434\u0440\u0443\u0437\u044c\u044f \u0441\u0442\u0430\u043b\u0438. \u041d\u0443 \u0438 \u0432 \u043a\u043e\u043d\u0446\u0435 \u043f\u043e\u043f\u0440\u043e\u0441\u0438\u043b \u0435\u0433\u043e \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u043e \u043e\u043f\u0438\u0441\u0430\u0442\u044c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043f\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0438 \u043f\u043e \u043f\u0430\u043c\u044f\u0442\u0438, \u0441 \u0447\u0435\u043c \u043e\u043d \u0442\u043e\u0436\u0435 \u0441\u043f\u0440\u0430\u0432\u0438\u043b\u0441\u044f. <\/p>\n
\n\u041a\u043e\u043d\u0435\u0447\u043d\u044b\u0439 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0439 \u043a\u043e\u0434<\/summary>\n
\n
from typing import List, Tuple, Dict, Optional, Any   # Time complexity: # The time complexity of this function is O(n), where n is the number of tuples in the input list. The reason is that # the function iterates over the input list once and performs a constant number of # operations (a constant number of type and value comparisons) on each element of the list. Therefore, # the total number of operations is directly proportional to the number of elements in the list, which makes # the time complexity O(n). # # Space complexity: # The space complexity of this function is O(n). The reason is that the function creates two data structures: tree and # children both with the same size as the number of tuples in the input list, to store the tree and # children of the each node. Therefore, the space complexity is directly proportional to the number of # elements in the input list, which makes the space complexity O(n). def to_tree(data: List[Tuple[Optional[str], str]]) -> Dict[str, Any]:     if not data:         return {}     if not isinstance(data, list):         raise ValueError(\"Input data must be a list\")     if not all(map(lambda x: isinstance(x, tuple), data)):         raise ValueError(\"Input data must be a list of tuples\")     if not all(map(lambda x: len(x) == 2, data)):         raise ValueError(\"Each tuple in input data must have exactly two elements\")     if not all(map(lambda x: (isinstance(x[0], str) or x[0] is None) and isinstance(x[1], str), data)):         raise ValueError(\"Parent and offspring must be of type str, parent can be None\")     check_offspring_duplicates(data)      children: Dict[str, Any] = {o: {} for _, o in data}     tree: Dict[str, Any] = {}     for parent, offspring in data:         if parent is None:             tree[offspring] = children[offspring]         else:             children[parent][offspring] = children[offspring]     return tree   # Time complexity of this function is O(n) as it also iterates over the input list once, and does a constant number of # dictionary operations on each iteration, where n is the number of tuples in the input list. # # Space complexity of this function is O(n) too, where n is the number of unique offsprings in the input data, because # it's creating a dictionary to store the count of each offspring and the size of the dictionary will be equal to the # number of unique offsprings in the input data. def check_offspring_duplicates(data: List[Tuple[Optional[str], str]]):     offspring_count = {}     for _, offspring in data:         if offspring in offspring_count:             offspring_count[offspring] += 1             raise ValueError(f\"Offspring {offspring} appears more than once in the input data\")         else:             offspring_count[offspring] = 1   # Tests  def test_to_tree():     source = [         (None, 'a'),         (None, 'b'),         (None, 'c'),         ('a', 'a1'),         ('a', 'a2'),         ('a2', 'a21'),         ('a2', 'a22'),         ('b', 'b1'),         ('b1', 'b11'),         ('b11', 'b111'),         ('b', 'b2'),         ('c', 'c1'),     ]      expected = {         'a': {'a1': {}, 'a2': {'a21': {}, 'a22': {}}},         'b': {'b1': {'b11': {'b111': {}}}, 'b2': {}},         'c': {'c1': {}},     }      assert to_tree(source) == expected   def test_to_tree_with_invalid_inputs():     # Test with empty input     assert to_tree([]) == {}      # Test with non-tuple input     try:         to_tree(['a', 'b'])     except ValueError as v:         assert str(v) == \"Input data must be a list of tuples\"     else:         assert False      # Test with tuple input with wrong number of elements     try:         to_tree([(None,), ('a', 'b', 'c')])     except ValueError as v:         assert str(v) == \"Each tuple in input data must have exactly two elements\"     else:         assert False   def test_input_type():     # Test input with non-string parent and offspring     try:         to_tree([(None, 1), (1, 2)])     except ValueError as v:         assert str(v) == \"Parent and offspring must be of type str, parent can be None\"     else:         assert False      try:         to_tree([(None, '1'), ('1', 2)])     except ValueError as v:         assert str(v) == \"Parent and offspring must be of type str, parent can be None\"     else:         assert False      try:         to_tree([(None, None)])     except ValueError as v:         assert str(v) == \"Parent and offspring must be of type str, parent can be None\"     else:         assert False      # Test input with string parent and offspring     assert to_tree([(None, '1'), ('1', '2')]) == {'1': {'2': {}}}   def test_duplicate_nodes():      try:         to_tree([(None, '1'), ('1', '2'), ('1', '2')])     except ValueError as v:         assert str(v) == \"Offspring 2 appears more than once in the input data\"     else:         assert False      try:         to_tree([(None, '1'), ('1', '2'), ('2', '2')])     except ValueError as v:         assert str(v) == \"Offspring 2 appears more than once in the input data\"     else:         assert False   test_to_tree() test_input_type() test_to_tree_with_invalid_inputs() test_duplicate_nodes()   <\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
\u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0448\u0442\u0440\u0438\u0445\u0438, \u043d\u043e \u0442\u0443\u0442 chatGPT \u0443\u0441\u0442\u0430\u043b — \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432 \u0432 \u0447\u0430\u0441, \u0442\u0430\u043a \u0438 \u0441\u043a\u0430\u0437\u0430\u043b<\/em>. <\/p>\n
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  <\/div>\n
  
 \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438  https:\/\/habr.com\/ru\/post\/710169\/<\/a>
<\/br><\/br><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-343818","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/343818","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=343818"}],"version-history":[{"count":0,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/343818\/revisions"}],"wp:attachment":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=343818"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=343818"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=343818"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}