


def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict      ):






							"""simple docstring"""



							return " ".join(sorted(set(sentence.split()      )      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



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


