





from __future__ import annotations

from collections import Counter
from random import random





class lowerCamelCase						:







  def __init__(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[str]					     =     {}







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	int					     =     {}







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        if nodea not in self.connections:
              self.add_node(UpperCamelCase__		)
        if nodea not in self.connections:
              self.add_node(UpperCamelCase__		)
        _snake_case		:	Tuple					     =     probability







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        return list(self.connections		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Dict					     =     0
        _snake_case		:	List[Any]					     =     random()

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Union[str, Any]					     =     MarkovChainGraphUndirectedUnweighted()

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

      _snake_case		:	List[str]					     =     Counter(graph.get_nodes()	)
      _snake_case		:	Optional[Any]					     =     start

      for _ in range(lowerCAmelCase__	):
            _snake_case		:	Dict					     =     graph.transition(lowerCAmelCase__	)
            visited[node] += 1

      return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()









'''simple docstring'''

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     torch.nn.Linear(10     ,     10		)
        _snake_case		:	Optional[int]					     =     torch.optim.SGD(model.parameters()     ,     0.1		)
        _snake_case		:	List[str]					     =     Accelerator()
        _snake_case		:	Optional[Any]					     =     accelerator.prepare(lowercase__		)
        try:
              pickle.loads(pickle.dumps(lowercase__		)		)
        except Exception as e:
              self.fail(F'''Accelerated optimizer pickling failed with {e}'''		)
        AcceleratorState._reset_state()


