

def      A__					(			__A  :						List[str]			)					->str:
      return [
          {
              0: [1, 2],
              1: [0, 2],
              2: [0, 1, 3, 5],
              3: [2, 4],
              4: [3],
              5: [2, 6, 8],
              6: [5, 7],
              7: [6, 8],
              8: [5, 7],
          },
          {
              0: [6],
              1: [9],
              2: [4, 5],
              3: [4],
              4: [2, 3],
              5: [2],
              6: [0, 7],
              7: [6],
              8: [],
              9: [1],
          },
          {
              0: [4],
              1: [6],
              2: [],
              3: [5, 6, 7],
              4: [0, 6],
              5: [3, 8, 9],
              6: [1, 3, 4, 7],
              7: [3, 6, 8, 9],
              8: [5, 7],
              9: [5, 7],
          },
          {
              0: [1, 3],
              1: [0, 2, 4],
              2: [1, 3, 4],
              3: [0, 2, 4],
              4: [1, 2, 3],
          },
      ][index]





def      A__					(			__A  :						dict[int, list[int]]			)					->list[tuple[int, int]]:
      __A    =0
      __A    =len(__A			)  # No of vertices in graph
      __A    =[0] * n
      __A    =[False] * n

      def dfs(__A  :						Any     ,				__A  :						int     ,				__A  :						str     ,				__A  :						int			):
            __A    =True
            __A    =id_
            id_ += 1
            for to in graph[at]:
                  if to == parent:
                        pass
                  elif not visited[to]:
                        dfs(__A     ,				__A     ,				__A     ,				id_			)
                        __A    =min(low[at]     ,				low[to]			)
                        if id_ <= low[to]:
                              bridges.append((at, to) if at < to else (to, at)			)
                  else:
                        # This edge is a back edge and cannot be a bridge
                        __A    =min(low[at]     ,				low[to]			)

      __A    =[]
      for i in range(__A			):
            if not visited[i]:
                  dfs(__A     ,				-1     ,				__A     ,				id_			)
      return bridges


if __name__ == "__main__":
   import doctest

   doctest.testmod()





from typing import TYPE_CHECKING

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


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


