



'''simple docstring'''







from __future__ import annotations

from queue import Queue

class    lowerCamelCase_				:






   '''simple docstring'''

   def __init__(	self			):
       UpperCamelCase__	     =      {}


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


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       if from_vertex in self.vertices:
           self.vertices[from_vertex].append(__UpperCamelCase			)
       else:
           UpperCamelCase__	     =      [to_vertex]







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      set()

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

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

       while not queue.empty():
           UpperCamelCase__	     =      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(__UpperCamelCase			)
                   visited.add(__UpperCamelCase			)
       return visited


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

      testmod(verbose=True)

      lowercase__  :	Optional[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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__  :	Optional[Any]            =      {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[Any]            =      ['MarianTokenizer']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[str]            =      [
          'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MarianForCausalLM',
          'MarianModel',
          'MarianMTModel',
          'MarianPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Optional[Any]            =      ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Any            =      ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
      from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_marian import MarianTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_marian import (
                MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarianForCausalLM,
                MarianModel,
                MarianMTModel,
                MarianPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
      import sys

      lowercase__  :	Any            =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




