"""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


UpperCAmelCase__  		=						datasets.utils.logging.get_logger(__name__)


@dataclass
class        a				(		datasets.BuilderConfig   ):
    _snake_case     :  int     							=							1_00_00
    _snake_case     :  Optional[List[str]]     							=							None
    _snake_case     :  Optional[datasets.Features]     							=							None



class        a				(		datasets.ArrowBasedBuilder   ):
    _snake_case     :  Tuple     							=							ParquetConfig


    def      lowerCAmelCase_  (					self	:  Dict					):
      return datasets.DatasetInfo(features=self.config.features					)


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


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  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						 =      table_cast(__lowerCAmelCase   ,				self.info.features.arrow_schema					)
      return pa_table


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      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:
          _UpperCAmelCase						 =      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					)					):
              _UpperCAmelCase						 =      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

"""simple docstring"""


def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  _UpperCAmelCase						 =      0
  while n > 0:
    res += n % 10
    n //= 10
  return res






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  return n if n < 10 else n % 10 + sum_of_digits(n // 10		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return sum(int(lowercase		) for c in str(abs(lowercase		)		)		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(lowercase   ,lowercase		) -> None:
    _UpperCAmelCase						 =      f'''{func.__name__}({value})'''
    _UpperCAmelCase						 =      timeit(f'''__main__.{call}'''   ,setup="""import __main__"""		)
    print(f'''{call:56} = {func(lowercase		)} -- {timing:.4f} seconds'''		)

  for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
    for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
      benchmark_a_function(lowercase   ,lowercase		)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
