
import sys
from collections import defaultdict




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      []



	def 			A							(  self     :   Tuple      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				return self.node_position[vertex]



	def 			A							(  self     :   Tuple      ,  lowercase     :   List[str]      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      pos



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Any     ):


				'''simple docstring'''


				if start > size // 2 - 1:
							return
				else:
							if 2 * start + 2 >= size:
										_snake_case          =      2 * start + 1
							else:
										if heap[2 * start + 1] < heap[2 * start + 2]:
													_snake_case          =      2 * start + 1
										else:
													_snake_case          =      2 * start + 2
							if heap[smallest_child] < heap[start]:
										_snake_case     , _snake_case          =      heap[smallest_child], positions[smallest_child]
										_snake_case     , _snake_case          =      (
										    heap[start],
										    positions[start],
										)
										_snake_case     , _snake_case          =      temp, tempa

										_snake_case          =      self.get_position(positions[smallest_child]     )
										self.set_position(
										    positions[smallest_child]      ,  self.get_position(positions[start]     )     )
										self.set_position(positions[start]      ,  lowercase     )

										self.top_to_bottom(lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )



	def 			A							(  self     :   Dict      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      position[index]

				while index != 0:
							_snake_case          =      int((index - 2) / 2     ) if index % 2 == 0 else int((index - 1) / 2     )

							if val < heap[parent]:
										_snake_case          =      heap[parent]
										_snake_case          =      position[parent]
										self.set_position(position[parent]      ,  lowercase     )
							else:
										_snake_case          =      val
										_snake_case          =      temp
										self.set_position(lowercase      ,  lowercase     )
										break
							_snake_case          =      parent
				else:
							_snake_case          =      val
							_snake_case          =      temp
							self.set_position(lowercase      ,  0     )



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Dict      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      len(lowercase     ) // 2 - 1
				for i in range(lowercase      ,  -1      ,  -1     ):
							self.top_to_bottom(lowercase      ,  lowercase      ,  len(lowercase     )      ,  lowercase     )





	def 			A							(  self     :   int      ,  lowercase     :   List[Any]      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      positions[0]
				_snake_case          =      sys.maxsize
				self.top_to_bottom(lowercase      ,  0      ,  len(lowercase     )      ,  lowercase     )
				return temp






def 	a_		(	__lowercase       :     Optional[int]			)			->  str:
			_snake_case          =      Heap()

			_snake_case          =      [0] * len(__lowercase			)
			_snake_case          =      [-1] * len(__lowercase			)  # Neighboring Tree Vertex of selected vertex
			# Minimum Distance of explored vertex with neighboring vertex of partial tree
			# formed in graph
			_snake_case          =      []  # Heap of Distance of vertices from their neighboring vertex
			_snake_case          =      []

			for vertex in range(len(__lowercase			)			):
						distance_tv.append(sys.maxsize			)
						positions.append(__lowercase			)
						heap.node_position.append(__lowercase			)

			_snake_case          =      []
			_snake_case          =      1
			_snake_case          =      sys.maxsize
			for neighbor, distance in adjacency_list[0]:
						_snake_case          =      0
						_snake_case          =      distance
			heap.heapify(__lowercase	, __lowercase			)

			for _ in range(1	, len(__lowercase			)			):
						_snake_case          =      heap.delete_minimum(__lowercase	, __lowercase			)
						if visited[vertex] == 0:
									tree_edges.append((nbr_tv[vertex], vertex)			)
									_snake_case          =      1
									for neighbor, distance in adjacency_list[vertex]:
												if (
												    visited[neighbor] == 0
												    and distance < distance_tv[heap.get_position(__lowercase			)]
												):
															_snake_case          =      distance
															heap.bottom_to_top(
															    __lowercase	, heap.get_position(__lowercase			)	, __lowercase	, __lowercase			)
															_snake_case          =      vertex
			return tree_edges


if __name__ == "__main__":  # pragma: no cover
			# < --------- Prims Algorithm --------- >
			_lowerCamelCase	:    Any          =      int(input('''Enter number of edges: ''').strip())
			_lowerCamelCase	:    Union[str, Any]          =      defaultdict(list)
			for _ in range(edges_number):
						_lowerCamelCase	:    str          =      [int(x) for x in input().strip().split()]
						adjacency_list[edge[0]].append([edge[1], edge[2]])
						adjacency_list[edge[1]].append([edge[0], edge[2]])
			print(prisms_algorithm(adjacency_list))

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()
				_snake_case          =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
				_dump_articles(lowercase      ,  lowercase     )

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
				    'de': [
				        'Maschinelles Lernen ist großartig, oder?',
				        'Ich esse gerne Bananen',
				        'Morgen ist wieder ein toller Tag!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
				testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']     )

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )