




"""simple docstring"""





A   =       {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			list[str]:


			"""simple docstring"""
			__UpperCAmelCase	: Any		=					set()
			# keep track of all the paths to be checked
			__UpperCAmelCase	: List[Any]		=					[[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						__UpperCAmelCase	: Optional[Any]		=					queue.pop(0							)
						# get the last node from the path
						__UpperCAmelCase	: List[Any]		=					path[-1]
						if node not in explored:
									__UpperCAmelCase	: List[Any]		=					graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__UpperCAmelCase	: Union[str, Any]		=					list(UpperCamelCase							)
												new_path.append(UpperCamelCase							)
												queue.append(UpperCamelCase							)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(UpperCamelCase							)

    # in case there's no path between the 2 nodes
			return []

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__UpperCAmelCase	: Dict		=					[start]
			__UpperCAmelCase	: Any		=					set(UpperCamelCase							)
			# Keep tab on distances from `start` node.
			__UpperCAmelCase	: List[Any]		=					{start: 0, target: -1}
			while queue:
						__UpperCAmelCase	: Dict		=					queue.pop(0							)
						if node == target:
									__UpperCAmelCase	: Tuple		=					(
									    dist[node] if dist[target] == -1 else min(dist[target]				,       dist[node]							)
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(UpperCamelCase							)
												queue.append(UpperCamelCase							)
												__UpperCAmelCase	: int		=					dist[node] + 1
			return dist[target]


if __name__ == "__main__":
			print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
			print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4








"""simple docstring"""





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__				(	__magic_name__   ):


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    pyspark.sql.DataFrame      ,  UpperCamelCase_      :    Optional[NamedSplit] = None      ,  UpperCamelCase_      :    Optional[Features] = None      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    str = None      ,  UpperCamelCase_      :    bool = False      ,  UpperCamelCase_      :    str = None      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    str = "arrow"      ,  **UpperCamelCase_      :    Tuple      ,  ):


						"""simple docstring"""




						super().__init__(
						    split=UpperCamelCase_      ,  features=UpperCamelCase_      ,  cache_dir=UpperCamelCase_      ,  keep_in_memory=UpperCamelCase_      ,  streaming=UpperCamelCase_      ,  **UpperCamelCase_      ,  )
						__UpperCAmelCase	: List[Any]		=					load_from_cache_file
						__UpperCAmelCase	: List[Any]		=					file_format
						__UpperCAmelCase	: str		=					Spark(
						    df=UpperCamelCase_      ,  features=UpperCamelCase_      ,  cache_dir=UpperCamelCase_      ,  working_dir=UpperCamelCase_      ,  **UpperCamelCase_      ,  )


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						if self.streaming:
									return self.builder.as_streaming_dataset(split=self.split)
						__UpperCAmelCase	: List[Any]		=					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)


