



from __future__ import annotations

from collections import Counter
from random import random





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		{}







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		{}







     def 						_a      (self				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           if nodea not in self.connections:
                 self.add_node(__a    )
           if nodea not in self.connections:
                 self.add_node(__a    )
           lowerCamelCase								=		probability







     def 						_a      (self    ):






           '''simple docstring'''



           return list(self.connections    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		0
           lowerCamelCase								=		random()

           for dest in self.connections[node]:
                 current_probability += self.connections[node][dest]
                 if current_probability > random_value:
                       return dest
           return ""



def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		MarkovChainGraphUndirectedUnweighted()

      for nodea, nodea, probability in transitions:
            graph.add_transition_probability(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      lowerCamelCase								=		Counter(graph.get_nodes() )
      lowerCamelCase								=		start

      for _ in range(UpperCAmelCase__ ):
            lowerCamelCase								=		graph.transition(UpperCAmelCase__ )
            visited[node] += 1

      return visited


if __name__ == "__main__":
     import doctest

     doctest.testmod()




from typing import TYPE_CHECKING

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


a_						:     int	     =		{'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Dict	     =		[
         'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'VanForImageClassification',
         'VanModel',
         'VanPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     a_						:     Union[str, Any]	     =		_LazyModule(__name__, globals()['__file__'], _import_structure)