code: 
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class 					snake_case__  :
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	data
  								a_       :   Node | None			=	None
  
  
  
  
  
  	def __str__(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return f"""{self.data}"""
  
  
  
  
  
  class 					snake_case__  :
  
  
  
  
  
  	def __init__(  self							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Node | None			=	None
  								a_       :   Node | None			=	None
  
  
  
  
  
  	def __iter__(  self							)		->			Iterator[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	self.front
  								while node:
  															yield node.data
  															a_       :   Tuple			=	node.next
  
  
  
  
  
  	def __len__(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(tuple(iter(self							)							)							)
  
  
  
  
  
  	def __str__(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return " <- ".join(str(UpperCamelCase_							) for item in self							)
  
  
  
  
  
  	def     A				(  self							)		->			bool:
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self							) == 0
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	Node(UpperCamelCase_							)
  								if self.is_empty():
  															a_       :   Tuple			=	node
  								else:
  															assert isinstance(self.rear				,    UpperCamelCase_							)
  															a_       :   str			=	node
  															a_       :   Any			=	node
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.is_empty():
  															raise IndexError("""dequeue from empty queue"""							)
  								assert isinstance(self.front				,    UpperCamelCase_							)
  								a_       :   List[Any]			=	self.front
  								a_       :   Tuple			=	self.front.next
  								if self.front is None:
  															a_       :   Dict			=	None
  								return node.data
  
  
  
  
  
  	def     A				(  self							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	None
  
  
  if __name__ == "__main__":
  	from doctest import testmod
  
  	testmod()
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
  
  
  