code: 
  
  
  from __future__ import annotations
  
  from queue import Queue
  
  
  
  
  
  class 					snake_case__  :
  
  
  
  
  
  	def __init__(  self							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   dict[int, list[int]]			=	{}
  
  
  
  
  
  	def     A				(  self							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								for i in self.vertices:
  															print(UpperCamelCase_				,    """ : """				,    """ -> """.join([str(UpperCamelCase_							) for j in self.vertices[i]]							)							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								if from_vertex in self.vertices:
  															self.vertices[from_vertex].append(UpperCamelCase_							)
  								else:
  															a_       :   List[str]			=	[to_vertex]
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			set[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	set()
  
  								# create a first in first out queue to store all the vertices for BFS
  								a_       :   Queue			=	Queue()
  
  								# mark the source node as visited and enqueue it
  								visited.add(UpperCamelCase_							)
  								queue.put(UpperCamelCase_							)
  
  								while not queue.empty():
  															a_       :   List[Any]			=	queue.get()
  
  															# loop through all adjacent vertex and enqueue it if not yet visited
  															for adjacent_vertex in self.vertices[vertex]:
  																						if adjacent_vertex not in visited:
  																													queue.put(UpperCamelCase_							)
  																													visited.add(UpperCamelCase_							)
  								return visited
  
  
  if __name__ == "__main__":
  	from doctest import testmod
  
  	testmod(verbose=True)
  
  	SCREAMING_SNAKE_CASE : List[str]          =    Graph()
  	g.add_edge(0, 1)
  	g.add_edge(0, 2)
  	g.add_edge(1, 2)
  	g.add_edge(2, 0)
  	g.add_edge(2, 3)
  	g.add_edge(3, 3)
  
  	g.print_graph()
  	# 0  :  1 -> 2
  	# 1  :  2
  	# 2  :  0 -> 3
  	# 3  :  3
  
  	assert sorted(g.bfs(2)) == [0, 1, 2, 3]
  
  
  
style_context: 
  
  
  import itertools
  from dataclasses import dataclass
  from typing import Optional
  
  import pyarrow as pa
  
  import datasets
  from datasets.table import table_cast
  
  
  SCREAMING_SNAKE_CASE : int          =    datasets.utils.logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class 					snake_case__  ( datasets.BuilderConfig       ):
  	UpperCAmelCase   :							Optional[datasets.Features]         = None
  
  
  
  
  
  class 					snake_case__  ( datasets.ArrowBasedBuilder       ):
  	UpperCAmelCase   :							Optional[int]         = ArrowConfig
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return datasets.DatasetInfo(features=self.config.features							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not self.config.data_files:
  															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
  								a_       :   List[str]			=	dl_manager.download_and_extract(self.config.data_files							)
  								if isinstance(UpperCamelCase_				,    (str, list, tuple)							):
  															a_       :   Dict			=	data_files
  															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  																						a_       :   Optional[Any]			=	[files]
  															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
  															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
  															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"""files""": files}							)]
  								a_       :   Any			=	[]
  								for split_name, files in data_files.items():
  															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  																						a_       :   Dict			=	[files]
  															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
  															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
  															# Infer features is they are stoed in the arrow schema
  															if self.info.features is None:
  																						for file in itertools.chain.from_iterable(UpperCamelCase_							):
  																													with open(UpperCamelCase_				,    """rb"""							) as f:
  																																				a_       :   List[str]			=	datasets.Features.from_arrow_schema(pa.ipc.open_stream(UpperCamelCase_							).schema							)
  																													break
  															splits.append(datasets.SplitGenerator(name=UpperCamelCase_				,    gen_kwargs={"""files""": files}							)							)
  								return splits
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			pa.Table:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.info.features is not None:
  															# more expensive cast to support nested features with keys in a different order
  															# allows str <-> int/float or str to Audio for example
  															a_       :   int			=	table_cast(UpperCamelCase_				,    self.info.features.arrow_schema							)
  								return pa_table
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_							)							):
  															with open(UpperCamelCase_				,    """rb"""							) as f:
  																						try:
  																													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(UpperCamelCase_							)							):
  																																				a_       :   Tuple			=	pa.Table.from_batches([record_batch]							)
  																																				# Uncomment for debugging (will print the Arrow table size and elements)
  																																				# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
  																																				# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
  																																				yield f"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase_							)
  																						except ValueError as e:
  																													logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase_							)}: {e}"""							)
  																													raise
  
  
  