
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


UpperCAmelCase__				:  Optional[int]       					=				datasets.utils.logging.get_logger(__name__)



@dataclass
class 			UpperCamelCase_			(						datasets.BuilderConfig   ):




							'''simple docstring'''

							UpperCamelCase_					   =							None



class 			UpperCamelCase_			(						datasets.ArrowBasedBuilder   ):




							'''simple docstring'''

							UpperCamelCase_					   =							ArrowConfig
							def        lowerCAmelCase__ (				self) ->	List[str]:
										return datasets.DatasetInfo(features=self.config.features)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Optional[Any]:
										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__    :		Union[str, Any]     =				dl_manager.download_and_extract(self.config.data_files)
										if isinstance(UpperCamelCase     ,						(str, list, tuple)):
													UpperCamelCase__    :		Optional[Any]     =				data_files
													if isinstance(UpperCamelCase     ,						UpperCamelCase):
																UpperCamelCase__    :		List[str]     =				[files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													UpperCamelCase__    :		Optional[Any]     =				[dl_manager.iter_files(UpperCamelCase) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,						gen_kwargs={'files': files})]
										UpperCamelCase__    :		List[str]     =				[]
										for split_name, files in data_files.items():
													if isinstance(UpperCamelCase     ,						UpperCamelCase):
																UpperCamelCase__    :		Any     =				[files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													UpperCamelCase__    :		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:
																						UpperCamelCase__    :		Optional[int]     =				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        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	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__    :		Tuple     =				table_cast(UpperCamelCase     ,						self.info.features.arrow_schema)
										return pa_table






							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										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)):
																						UpperCamelCase__    :		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(UpperCamelCase)
																except ValueError as e:
																			logger.error(F"""Failed to read file '{file}' with error {type(UpperCamelCase)}: {e}""")
																			raise





from __future__ import annotations

import re



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     list[str]:

			def alphanum_key(__SCREAMING_SNAKE_CASE ):
						return [int(__SCREAMING_SNAKE_CASE ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'				,    __SCREAMING_SNAKE_CASE )]

			return sorted(__SCREAMING_SNAKE_CASE				,    key=__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



