





def 	A_       (  _lowerCAmelCase  )		->				int:
 if not isinstance(_lowerCAmelCase							,     _lowerCAmelCase  ) or number < 0:
  raise ValueError("Input must be a non-negative integer"  )

 UpperCamelCase			:      int			=     0
 while number:
  # This way we arrive at next set bit (next 1) instead of looping
  # through each bit and checking for 1s hence the
  # loop won't run 32 times it will only run the number of `1` times
  number &= number - 1
  count += 1
 return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()







from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = None ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           features=A_ ,							cache_dir=A_ ,							keep_in_memory=A_ ,							streaming=A_ ,							num_proc=A_ ,							**A_ ,							)
       UpperCamelCase			:      Optional[int] 			=     Generator(
           cache_dir=A_ ,							features=A_ ,							generator=A_ ,							gen_kwargs=A_ ,							**A_ ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.streaming:
        UpperCamelCase			:      Optional[Any] 			=     self.builder.as_streaming_dataset(split="train"     )
       # Build regular (map-style) dataset
       else:
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      List[Any] 			=     None
        UpperCamelCase			:      List[str] 			=     None

        self.builder.download_and_prepare(
            download_config=A_ ,							download_mode=A_ ,							verification_mode=A_ ,							base_path=A_ ,							num_proc=self.num_proc ,							)
        UpperCamelCase			:      int 			=     self.builder.as_dataset(
            split="train" ,							verification_mode=A_ ,							in_memory=self.keep_in_memory     )
       return dataset
