from __future__ import annotations




class 					_snake_case						:
					def __init__(  self:				List[Any]	,  __lowerCamelCase:				int = 0     )    ->				None:
												__UpperCAmelCase						:			Dict         =   value
												__UpperCAmelCase						:			Node | None         =   None
												__UpperCAmelCase						:			Node | None         =   None




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Node | None:
							if treea is None:
														return treea
							if treea is None:
														return treea

							__UpperCAmelCase						:			Tuple         =   treea.value + treea.value
							__UpperCAmelCase						:			Optional[Any]         =   merge_two_binary_trees(treea.left,    treea.left							)
							__UpperCAmelCase						:			List[str]         =   merge_two_binary_trees(treea.right,    treea.right							)
							return treea




def   _UpperCamelCase   (			snake_case__							)						->   None:
							if root:
														print(root.value							)
														print_preorder(root.left							)
														print_preorder(root.right							)


if __name__ == "__main__":
	_snake_case			=						Node(1)
	_snake_case			=						Node(2)
	_snake_case			=						Node(3)
	_snake_case			=						Node(4)

	_snake_case			=						Node(2)
	_snake_case			=						Node(4)
	_snake_case			=						Node(6)
	_snake_case			=						Node(9)
	_snake_case			=						Node(5)

	print('''Tree1 is: ''')
	print_preorder(treea)
	print('''Tree2 is: ''')
	print_preorder(treea)
	_snake_case			=						merge_two_binary_trees(treea, treea)
	print('''Merged Tree is: ''')
	print_preorder(merged_tree)





import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


_snake_case			=						datasets.utils.logging.get_logger(__name__)




@dataclass
class 					_snake_case						(						datasets.BuilderConfig				):
					lowerCamelCase__:		Optional[datasets.Features]  =     None




class 					_snake_case						(						datasets.ArrowBasedBuilder				):
					lowerCamelCase__:		Union[str, Any]  =     ArrowConfig
					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Optional[int]:
												return datasets.DatasetInfo(features=self.config.features     )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				Tuple     )    ->				str:
												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}'''     )
												__UpperCAmelCase						:			Dict         =   dl_manager.download_and_extract(self.config.data_files     )
												if isinstance(__lowerCamelCase	,  (str, list, tuple)     ):
																			__UpperCAmelCase						:			str         =   data_files
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			List[str]         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"files": files}     )]
												__UpperCAmelCase						:			Union[str, Any]         =   []
												for split_name, files in data_files.items():
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			Tuple         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) 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(__lowerCamelCase     ):
																																	with open(__lowerCamelCase	,  "rb"     ) as f:
																																								__UpperCAmelCase						:			Union[str, Any]         =   datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowerCamelCase     ).schema     )
																																	break
																			splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,  gen_kwargs={"files": files}     )     )
												return splits
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				pa.Table     )    ->				pa.Table:
												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
																			__UpperCAmelCase						:			List[str]         =   table_cast(__lowerCamelCase	,  self.info.features.arrow_schema     )
												return pa_table





					def    _lowerCamelCase (  self:				Any	,  __lowerCamelCase:				Any     )    ->				Any:
												for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase     )     ):
																			with open(__lowerCamelCase	,  "rb"     ) as f:
																										try:
																																	for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowerCamelCase     )     ):
																																								__UpperCAmelCase						:			Optional[int]         =   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(__lowerCamelCase     )
																										except ValueError as e:
																																	logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase     )}: {e}'''     )
																																	raise




