






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class      __A(    lowerCamelCase_			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      **SCREAMING_SNAKE_CASE_	,      ):
       super().__init__(
           features=lowerCAmelCase__	,      cache_dir=lowerCAmelCase__	,      keep_in_memory=lowerCAmelCase__	,      streaming=lowerCAmelCase__	,      num_proc=lowerCAmelCase__	,      **lowerCAmelCase__	,      )
       UpperCamelCase__				=    Generator(
           cache_dir=lowerCAmelCase__	,      features=lowerCAmelCase__	,      generator=lowerCAmelCase__	,      gen_kwargs=lowerCAmelCase__	,      **lowerCAmelCase__	,      )







  def 	UpperCAmelCase_ (self						):
       # Build iterable dataset
       if self.streaming:
            UpperCamelCase__				=    self.builder.as_streaming_dataset(split="""train"""						)
       # Build regular (map-style) dataset
       else:
            UpperCamelCase__				=    None
            UpperCamelCase__				=    None
            UpperCamelCase__				=    None
            UpperCamelCase__				=    None

            self.builder.download_and_prepare(
                download_config=lowerCAmelCase__	,      download_mode=lowerCAmelCase__	,      verification_mode=lowerCAmelCase__	,      base_path=lowerCAmelCase__	,      num_proc=self.num_proc	,      )
            UpperCamelCase__				=    self.builder.as_dataset(
                split="""train"""	,      verification_mode=lowerCAmelCase__	,      in_memory=self.keep_in_memory						)
       return dataset







from __future__ import annotations

from collections import Counter
from random import random




class      __A:

  """simple docstring"""







  def __init__(self						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    probability







  def 	UpperCAmelCase_ (self						):
       return list(self.connections						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    random()

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






def 						__magic_name__			(   __a      :	str	,		__a      :	list[tuple[str, str, float]]	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    MarkovChainGraphUndirectedUnweighted()

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

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

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

     return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()





