





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_A :   int  			=  logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   field(default=lowerCAmelCase_					,metadata={"help": "Whether to use SortishSampler or not."} )
       _UpperCAmelCase				:  str			   =   field(
           default=lowerCAmelCase_					,metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
       _UpperCAmelCase				:  Optional[Any]			   =   field(
           default=lowerCAmelCase_					,metadata={
               "help": (
                   "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                   "to the `max_length` value of the model configuration."
               )
           }					,)
       _UpperCAmelCase				:  List[Any]			   =   field(
           default=lowerCAmelCase_					,metadata={
               "help": (
                   "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                   "to the `num_beams` value of the model configuration."
               )
           }					,)
       _UpperCAmelCase				:  Union[str, Any]			   =   field(
           default=lowerCAmelCase_					,metadata={
               "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
           }					,)







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Tuple:
          lowerCamelCase__       :						str        =							super().to_dict()
          for k, v in d.items():
             if isinstance(_lowerCAmelCase		, _lowerCAmelCase      ):
                lowerCamelCase__       :						List[str]        =							v.to_dict()
          return d










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''					)

   lowerCamelCase__       :						List[str]        =							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()



