


"""simple docstring"""
from __future__ import annotations

from queue import Queue



class      _UpperCAmelCase					:






		'''simple docstring'''

		def __init__(self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							{}



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''simple docstring'''

			if from_vertex in self.vertices:
				self.vertices[from_vertex].append(A_   )
			else:
				__snake_case      :		Optional[int]         =							[to_vertex]



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							set()

			# create a first in first out queue to store all the vertices for BFS
			__snake_case      :		List[Any]         =							Queue()

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

			while not queue.empty():
				__snake_case      :		List[Any]         =							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(A_   )
						visited.add(A_   )
			return visited


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

			testmod(verbose=True)

			SCREAMING_SNAKE_CASE					:       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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__						      =			{
    '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:
     snake_case__						      =			[
         'DPRContextEncoderTokenizerFast',
         'DPRQuestionEncoderTokenizerFast',
         'DPRReaderTokenizerFast',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         '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:
     snake_case__						      =			[
         '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

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