





'''simple docstring'''




import heapq






def 			_lowerCAmelCase     (    __snake_case    :					dict     )	->	set[int]:
     __A		:					list[list]						=			[]

     # for each node and his adjacency list add them and the rank of the node to queue
     # using heapq module the queue will be filled like a Priority Queue
     # heapq works with a min priority queue, so I used -1*len(v) to build it
     for key, value in graph.items():
          # O(log(n))
          heapq.heappush(__snake_case					,				[-1 * len(__snake_case     ), (key, value)]     )

     # chosen_vertices = set of chosen vertices
     __A		:					Union[str, Any]						=			set()

     # while queue isn't empty and there are still edges
     #   (queue[0][0] is the rank of the node with max rank)
     while queue and queue[0][0] != 0:
          # extract vertex with max rank from queue and add it to chosen_vertices
          __A		:					Any						=			heapq.heappop(__snake_case     )[1][0]
          chosen_vertices.add(__snake_case     )

          # Remove all arcs adjacent to argmax
          for elem in queue:
               # if v haven't adjacent node, skip
               if elem[0] == 0:
                    continue
               # if argmax is reachable from elem
               # remove argmax from elem's adjacent list and update his rank
               if argmax in elem[1][1]:
                    __A		:					Dict						=			elem[1][1].index(__snake_case     )
                    del elem[1][1][index]
                    elem[0] += 1
        # re-order the queue
          heapq.heapify(__snake_case     )
     return chosen_vertices


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowercase__       : str         =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")






'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d