



from __future__ import annotations

from queue import Queue



class 		_lowercase		:

   def __init__(   self      :							Any				)							->					None:


    """simple docstring"""






    a							=		{}

   def 		A				(   self      :							Dict				)							->					None:


    """simple docstring"""






    for i in self.vertices:
     print(__lowerCAmelCase				, " : "				, " -> ".join([str(__lowerCAmelCase				) for j in self.vertices[i]]				)				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							int				)							->					None:


    """simple docstring"""






    if from_vertex in self.vertices:
     self.vertices[from_vertex].append(__lowerCAmelCase				)
    else:
     a							=		[to_vertex]




   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							int				)							->					set[int]:


    """simple docstring"""






    a							=		set()

    # create a first in first out queue to store all the vertices for BFS
    a							=		Queue()

    # mark the source node as visited and enqueue it
    visited.add(__lowerCAmelCase				)
    queue.put(__lowerCAmelCase				)

    while not queue.empty():
     a							=		queue.get()

     # loop through all adjacent vertex and enqueue it if not yet visited
     for adjacent_vertex in self.vertices[vertex]:
      if adjacent_vertex not in visited:
       queue.put(__lowerCAmelCase				)
       visited.add(__lowerCAmelCase				)
    return visited


if __name__ == "__main__":
     from doctest import testmod

     testmod(verbose=True)

     A_		:  Tuple										=  Graph()
     g.add_edge(0, 1)
     g.add_edge(0, 2)
     g.add_edge(1, 2)
     g.add_edge(2, 0)
     g.add_edge(2, 3)
     g.add_edge(3, 3)

     g.print_graph()
     # 0  :  1 -> 2
     # 1  :  2
     # 2  :  0 -> 3
     # 3  :  3

     assert sorted(g.bfs(2)) == [0, 1, 2, 3]






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_		:  Any										=  {
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:  Optional[int]										=  [
         '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''AlignModel''',
         '''AlignPreTrainedModel''',
         '''AlignTextModel''',
         '''AlignVisionModel''',
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

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

