




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


__lowerCAmelCase	:       Tuple     =	datasets.utils.logging.get_logger(__name__)




@dataclass
class 					snake_case__							(datasets.BuilderConfig     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      1_00_00
				SCREAMING_SNAKE_CASE_ :							Optional[List[str]]											=      None
				SCREAMING_SNAKE_CASE_ :							Optional[datasets.Features]											=      None




class 					snake_case__							(datasets.ArrowBasedBuilder     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							str											=      ParquetConfig





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Dict:
							return datasets.DatasetInfo(features=self.config.features							)





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





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __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
										a					=    table_cast(__lowerCamelCase      ,   self.info.features.arrow_schema							)
							return pa_table





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Optional[Any]							)      ->  str:
							a					=    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(__lowerCamelCase							)							):
										with open(__lowerCamelCase      ,   "rb"							) as f:
													a					=    pq.ParquetFile(__lowerCamelCase							)
													try:
																for batch_idx, record_batch in enumerate(
																    parquet_file.iter_batches(batch_size=self.config.batch_size      ,   columns=self.config.columns							)							):
																			a					=    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












__lowerCAmelCase	:       List[Any]     =	{str(digit): digit**5 for digit in range(10)}




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A     )     )




def     __magic_name__       (				):






			'''simple docstring'''







			return sum(
			    number
			    for number in range(1000,						1000000     )
			    if number == digits_fifth_powers_sum(A     )     )


if __name__ == "__main__":
		print(solution())






