

'''simple docstring'''







import heapq as hq
import math
from collections.abc import Iterator


class        A__   :



    def __init__(       self		,					UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   str(id_							)
           A_						       =   None
           A_						       =   None
           A_						       =   []
           A_						       =   {}  # {vertex:distance}



    def __lt__(       self		,					UpperCamelCase__							)    ->   Optional[Any]:





           '''simple docstring'''
           return self.key < other.key



    def __repr__(       self							)    ->   Optional[Any]:





           '''simple docstring'''
           return self.id



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   Dict:





           '''simple docstring'''
           self.neighbors.append(UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   weight

def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       # add the neighbors:
       graph[a - 1].add_neighbor(graph[b - 1]					)
       graph[b - 1].add_neighbor(graph[a - 1]					)
       # add the edges:
       graph[a - 1].add_edge(graph[b - 1],							UpperCAmelCase__					)
       graph[b - 1].add_edge(graph[a - 1],							UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      list:
       A_						       =   []
       for u in graph:
              A_						       =   math.inf
              A_						       =   None
       A_						       =   0
       A_						       =   graph[:]
       while q:
              A_						       =   min(UpperCAmelCase__					)
              q.remove(UpperCAmelCase__					)
              for v in u.neighbors:
                     if (v in q) and (u.edges[v.id] < v.key):
                            A_						       =   u
                            A_						       =   u.edges[v.id]
       for i in range(1,							len(UpperCAmelCase__					)					):
              a.append((int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)					)
       return a
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Iterator[tuple]:
       for u in graph:
              A_						       =   math.inf
              A_						       =   None
       A_						       =   0

       A_						       =   list(UpperCAmelCase__					)
       hq.heapify(UpperCAmelCase__					)

       while h:
              A_						       =   hq.heappop(UpperCAmelCase__					)
              for v in u.neighbors:
                     if (v in h) and (u.edges[v.id] < v.key):
                            A_						       =   u
                            A_						       =   u.edges[v.id]
                            hq.heapify(UpperCAmelCase__					)

       for i in range(1,							len(UpperCAmelCase__					)					):
              yield (int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)
def    UpperCAmelCase__     (   )      ->      None:
       pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCamelCase									=     {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase									=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
