






"""simple docstring"""



import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class        lowerCAmelCase__       (  __magic_name__				,		unittest.TestCase					):
   SCREAMING_SNAKE_CASE_					      =BertweetTokenizer
   SCREAMING_SNAKE_CASE_					      =False
   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase__		:  List[Any]				=				["I", "m", "V@@", "R@@", "r", "e@@"]
        UpperCAmelCase__		:  Optional[Any]				=				dict(zip(snake_case__				,       range(len(snake_case__				)				)				)				)
        UpperCAmelCase__		:  List[str]				=				["#version: 0.2", "a m</w>"]
        UpperCAmelCase__		:  str				=				{"unk_token": "<unk>"}

        UpperCAmelCase__		:  int				=				os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES["vocab_file"]				)
        UpperCAmelCase__		:  str				=				os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES["merges_file"]				)
        with open(self.vocab_file				,       "w"				,       encoding="utf-8"				) as fp:
             for token in vocab_tokens:
                  fp.write(f'{token} {vocab_tokens[token]}\n'				)
        with open(self.merges_file				,       "w"				,       encoding="utf-8"				) as fp:
             fp.write("\n".join(snake_case__				)				)
   def 	__a				(						self	:     str				,       **snake_case__	:     Optional[Any]				):


        '''simple docstring'''
        kwargs.update(self.special_tokens_map				)
        return BertweetTokenizer.from_pretrained(self.tmpdirname				,       **snake_case__				)
   def 	__a				(						self	:     str				,       snake_case__	:     Union[str, Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				"I am VinAI Research"
        UpperCAmelCase__		:  Any				=				"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
        return input_text, output_text




   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				BertweetTokenizer(self.vocab_file				,       self.merges_file				,       **self.special_tokens_map				)
        UpperCAmelCase__		:  List[Any]				=				"I am VinAI Research"
        UpperCAmelCase__		:  Union[str, Any]				=				"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
        UpperCAmelCase__		:  str				=				tokenizer.tokenize(snake_case__				)
        self.assertListEqual(snake_case__				,       snake_case__				)

        UpperCAmelCase__		:  Optional[Any]				=				tokens + [tokenizer.unk_token]

        UpperCAmelCase__		:  Dict				=				[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)				,       snake_case__				)







from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
__A				    =						{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}



def        __a     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



						UpperCAmelCase_=   {node: {node} for node in graph}

						UpperCAmelCase_=   {node: graph[node][:] for node in graph}

						while len(lowerCAmelCase_				) > 2:
												# Choose a random edge.
												UpperCAmelCase_=   random.choice(list(graph_copy.keys()				)				)
												UpperCAmelCase_=   random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												UpperCAmelCase_=   u + v
												UpperCAmelCase_=   list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(lowerCAmelCase_				)
												uv_neighbors.remove(lowerCAmelCase_				)
												UpperCAmelCase_=   uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(lowerCAmelCase_				)

												UpperCAmelCase_=   set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)

    # Find cutset.
						UpperCAmelCase_=   [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))




