





from __future__ import annotations

from collections import Counter
from random import random



class  __lowerCAmelCase     :




 def __init__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    {}




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    {}




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''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        lowerCamelCase      (						self				):



    '''simple docstring'''
    return list(self.connections				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''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 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: int ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    MarkovChainGraphUndirectedUnweighted()

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

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

   for _ in range(lowercase_							):
      __lowerCamelCase													=    graph.transition(lowercase_							)
      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


a_			=					{
    """configuration_clipseg""": [
        """CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPSegConfig""",
        """CLIPSegTextConfig""",
        """CLIPSegVisionConfig""",
    ],
    """processing_clipseg""": ["""CLIPSegProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_			=					[
      """CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CLIPSegModel""",
      """CLIPSegPreTrainedModel""",
      """CLIPSegTextModel""",
      """CLIPSegVisionModel""",
      """CLIPSegForImageSegmentation""",
  ]

if TYPE_CHECKING:
  from .configuration_clipseg import (
      CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
      CLIPSegConfig,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
  )
  from .processing_clipseg import CLIPSegProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_clipseg import (
        CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
        CLIPSegForImageSegmentation,
        CLIPSegModel,
        CLIPSegPreTrainedModel,
        CLIPSegTextModel,
        CLIPSegVisionModel,
    )

else:
  import sys

  a_			=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






