






'''simple docstring'''







import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_A	:			Optional[Any]         =datasets.utils.logging.get_logger(__name__)

@dataclass
class _lowercase	(				datasets.BuilderConfig		):
							a				      =       1_0000
							a				      =       None
							a				      =       None



class _lowercase	(				datasets.ArrowBasedBuilder		):
							a				      =       ParquetConfig




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														return datasets.DatasetInfo(features=self.config.features     )




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				Optional[int]     ):
														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}'''     )
														lowerCamelCase__					:	Union[str, Any]         =       dl_manager.download_and_extract(self.config.data_files     )
														if isinstance(UpperCamelCase__			,							(str, list, tuple)     ):
																					lowerCamelCase__					:	Any         =       data_files
																					if isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																												lowerCamelCase__					:	Any         =       [files]
																					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																					lowerCamelCase__					:	Optional[int]         =       [dl_manager.iter_files(UpperCamelCase__     ) for file in files]
																					return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,							gen_kwargs={"""files""": files}     )]
														lowerCamelCase__					:	Dict         =       []
														for split_name, files in data_files.items():
																					if isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																												lowerCamelCase__					:	Any         =       [files]
																					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																					lowerCamelCase__					:	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:
																																										lowerCamelCase__					:	Optional[Any]         =       datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase__     )     )
																																			break
																					splits.append(datasets.SplitGenerator(name=UpperCamelCase__			,							gen_kwargs={"""files""": files}     )     )
														return splits




							def 		lowerCamelCase_				(							self:				List[str]			,							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
																					lowerCamelCase__					:	Optional[int]         =       table_cast(UpperCamelCase__			,							self.info.features.arrow_schema     )
														return pa_table




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	str         =       self.info.features.arrow_schema if self.info.features is not None else None
														if self.info.features is not None and self.config.columns is not None:
																					if sorted(field.name for field in schema     ) != sorted(self.config.columns     ):
																												raise ValueError(
																												    F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''     )
														for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__     )     ):
																					with open(UpperCamelCase__			,							"""rb"""     ) as f:
																												lowerCamelCase__					:	List[Any]         =       pq.ParquetFile(UpperCamelCase__     )
																												try:
																																			for batch_idx, record_batch in enumerate(
																																			    parquet_file.iter_batches(batch_size=self.config.batch_size			,							columns=self.config.columns     )     ):
																																										lowerCamelCase__					:	Optional[Any]         =       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









'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				list[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       [0] * len_array

														if len_array > 0:
																					lowerCamelCase__					:	Union[str, Any]         =       array[0]

														for i in range(1			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	Tuple         =       self.prefix_sum[i - 1] + array[i]




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														if start == 0:
																					return self.prefix_sum[end]

														return self.prefix_sum[end] - self.prefix_sum[start - 1]




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Dict         =       {0}
														for sum_item in self.prefix_sum:
																					if sum_item - target_sum in sums:
																												return True

																					sums.add(UpperCamelCase__     )

														return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()

