






from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__a							(  __A						):





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = "arrow" ,    **UpperCamelCase__ ,    ):
						super().__init__(
						    split=UpperCamelCase__ ,    features=UpperCamelCase__ ,    cache_dir=UpperCamelCase__ ,    keep_in_memory=UpperCamelCase__ ,    streaming=UpperCamelCase__ ,    **UpperCamelCase__ ,    )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   load_from_cache_file
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   file_format
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   Spark(
						    df=UpperCamelCase__ ,    features=UpperCamelCase__ ,    cache_dir=UpperCamelCase__ ,    working_dir=UpperCamelCase__ ,    **UpperCamelCase__ ,    )






		def   __snake_case			(						self       ):
						if self.streaming:
										return self.builder.as_streaming_dataset(split=self.split       )
						SCREAMING_SNAKE_CASE_   :	str													=   None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
						self.builder.download_and_prepare(
						    download_mode=UpperCamelCase__ ,    file_format=self._file_format ,    )
						return self.builder.as_dataset(split=self.split       )







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


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


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






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_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 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







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

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

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

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

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()