


from __future__ import annotations

from queue import Queue

class    __magic_name__						:


     def __init__(   self				:  int      )	->   None:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   None:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  int      )	->   None:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  int      )	->   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)

       __UpperCamelCase							:							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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase							:							Dict    							=					{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Any    							=					[
           "DPRContextEncoderTokenizerFast",
           "DPRQuestionEncoderTokenizerFast",
           "DPRReaderTokenizerFast",
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							str    							=					[
           "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPRContextEncoder",
           "DPRPretrainedContextEncoder",
           "DPRPreTrainedModel",
           "DPRPretrainedQuestionEncoder",
           "DPRPretrainedReader",
           "DPRQuestionEncoder",
           "DPRReader",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							int    							=					[
           "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFDPRContextEncoder",
           "TFDPRPretrainedContextEncoder",
           "TFDPRPretrainedQuestionEncoder",
           "TFDPRPretrainedReader",
           "TFDPRQuestionEncoder",
           "TFDPRReader",
       ]


if TYPE_CHECKING:
       from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
       from .tokenization_dpr import (
           DPRContextEncoderTokenizer,
           DPRQuestionEncoderTokenizer,
           DPRReaderOutput,
           DPRReaderTokenizer,
       )

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_dpr_fast import (
                  DPRContextEncoderTokenizerFast,
                  DPRQuestionEncoderTokenizerFast,
                  DPRReaderTokenizerFast,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dpr import (
                  DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPRContextEncoder,
                  DPRPretrainedContextEncoder,
                  DPRPreTrainedModel,
                  DPRPretrainedQuestionEncoder,
                  DPRPretrainedReader,
                  DPRQuestionEncoder,
                  DPRReader,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_dpr import (
                  TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDPRContextEncoder,
                  TFDPRPretrainedContextEncoder,
                  TFDPRPretrainedQuestionEncoder,
                  TFDPRPretrainedReader,
                  TFDPRQuestionEncoder,
                  TFDPRReader,
              )

else:
       import sys

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


