
from __future__ import annotations

from queue import Queue





class 		__lowerCamelCase     :



							def __init__(     self							)						->     None:






								"""simple docstring"""



								UpperCAmelCase:					dict[int, list[int]] 						=     {}



							def     A__ (     self							)						->     None:






								"""simple docstring"""



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



							def     A__ (     self	,				__snake_case	,				__snake_case							)						->     None:






								"""simple docstring"""



								if from_vertex in self.vertices:
									self.vertices[from_vertex].append(lowerCamelCase__							)
								else:
									UpperCAmelCase:					List[Any] 						=     [to_vertex]




							def     A__ (     self	,				__snake_case							)						->     set[int]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     set()

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

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

								while not queue.empty():
									UpperCAmelCase:					Dict 						=     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)

	snake_case_      :					Optional[int]				      =     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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase							:							List[Any]    							=					{
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					["MPNetTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					[
           "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MPNetForMaskedLM",
           "MPNetForMultipleChoice",
           "MPNetForQuestionAnswering",
           "MPNetForSequenceClassification",
           "MPNetForTokenClassification",
           "MPNetLayer",
           "MPNetModel",
           "MPNetPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							List[Any]    							=					[
           "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMPNetEmbeddings",
           "TFMPNetForMaskedLM",
           "TFMPNetForMultipleChoice",
           "TFMPNetForQuestionAnswering",
           "TFMPNetForSequenceClassification",
           "TFMPNetForTokenClassification",
           "TFMPNetMainLayer",
           "TFMPNetModel",
           "TFMPNetPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
       from .tokenization_mpnet import MPNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mpnet_fast import MPNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mpnet import (
                  MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MPNetForMaskedLM,
                  MPNetForMultipleChoice,
                  MPNetForQuestionAnswering,
                  MPNetForSequenceClassification,
                  MPNetForTokenClassification,
                  MPNetLayer,
                  MPNetModel,
                  MPNetPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mpnet import (
                  TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMPNetEmbeddings,
                  TFMPNetForMaskedLM,
                  TFMPNetForMultipleChoice,
                  TFMPNetForQuestionAnswering,
                  TFMPNetForSequenceClassification,
                  TFMPNetForTokenClassification,
                  TFMPNetMainLayer,
                  TFMPNetModel,
                  TFMPNetPreTrainedModel,
              )

else:
       import sys

       __UpperCamelCase							:							Tuple    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


