


'''simple docstring'''



from __future__ import annotations

from queue import Queue





class        UpperCAmelCase_  :





				"""simple docstring"""






				def __init__(							self     )    ->					None:
									__lowerCamelCase       :     List[str]        =		{}







				def 				lowercase_    (							self     )    ->					None:




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







				def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					None:




									if from_vertex in self.vertices:
														self.vertices[from_vertex].append(__SCREAMING_SNAKE_CASE     )
									else:
														__lowerCamelCase       :     Union[str, Any]        =		[to_vertex]







				def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					set[int]:
									__lowerCamelCase       :     str        =		set()

									# create a first in first out queue to store all the vertices for BFS
									__lowerCamelCase       :     Union[str, Any]        =		Queue()

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

									while not queue.empty():
														__lowerCamelCase       :     str        =		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(__SCREAMING_SNAKE_CASE     )
																								visited.add(__SCREAMING_SNAKE_CASE     )
									return visited


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

						testmod(verbose=True)

						A__		: Any												=  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]










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__		: str												=  {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: List[str]												=  [
          """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """HubertForCTC""",
          """HubertForSequenceClassification""",
          """HubertModel""",
          """HubertPreTrainedModel""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: Optional[int]												=  [
          """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFHubertForCTC""",
          """TFHubertModel""",
          """TFHubertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_hubert import (
                HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                HubertForCTC,
                HubertForSequenceClassification,
                HubertModel,
                HubertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_hubert import (
                TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFHubertForCTC,
                TFHubertModel,
                TFHubertPreTrainedModel,
            )


else:
      import sys

      A__		: Optional[int]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






