

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

class   _a					:




      """simple docstring"""

      def __init__(   self							:				Any						,			UpperCAmelCase							:				Dict	):
            A_						  =				str(id_	)
            A_						  =				None
            A_						  =				None
            A_						  =				[]
            A_						  =				{}  # {vertex:distance}





      def __lt__(   self							:				Dict						,			UpperCAmelCase							:				Union[str, Any]	):
            return self.key < other.key





      def __repr__(   self							:				Optional[int]	):
            return self.id





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				Optional[Any]	):
            self.neighbors.append(UpperCAmelCase	)







      def 		__A	(   self							:				int						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				int	):
            A_						  =				weight




def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: str				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Dict    ):



      """simple docstring"""

      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       __snake_case						( __UpperCamelCase	: list				,__UpperCamelCase	: Vertex    ):



      """simple docstring"""

      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       __snake_case						( __UpperCamelCase	: list				,__UpperCamelCase	: Vertex    ):



      """simple docstring"""

      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       __snake_case						( ):



      """simple docstring"""



if __name__ == "__main__":
      import doctest

      doctest.testmod()



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:  Tuple    		=      {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  List[str]    		=      [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  Union[str, Any]    		=      [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:  int    		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




