

lowerCamelCase           =					{'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowerCamelCase           =					['''a''', '''b''', '''c''', '''d''', '''e''']




def 			UpperCAmelCase__				(			_A      : Any	,      _A      : int	,      _A      : str							):





  '''simple docstring'''







  a__  =start
  # add current to visited
  visited.append(_A							)
  a__  =edges[current]
  for neighbor in neighbors:
    # if neighbor not in visited, visit
    if neighbor not in visited:
      a__  =topological_sort(_A	,      _A	,      _A							)
    # if all neighbors visited add current to sort
  sort.append(_A							)
  # if all vertices haven't been visited select a new one to visit
  if len(_A							) != len(_A							):
    for vertice in vertices:
      if vertice not in visited:
        a__  =topological_sort(_A	,      _A	,      _A							)
    # return sort
  return sort


if __name__ == "__main__":
       lowerCamelCase           =					topological_sort('''a''', [], [])
       print(sort)



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

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