


def __UpperCAmelCase       (		__A			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     abs(__A			)
       UpperCAmelCase__										=     0
       while n > 0:
              res += n % 1_0
              n //= 1_0
       return res





def __UpperCAmelCase       (		__A			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     abs(__A			)
       return n if n < 1_0 else n % 1_0 + sum_of_digits(n // 1_0			)





def __UpperCAmelCase       (		__A			)						->					int:



       '''simple docstring'''





       return sum(int(__A			) for c in str(abs(__A			)			)			)





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       from collections.abc import Callable
       from timeit import timeit

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

       for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
              for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
                     benchmark_a_function(__A ,						__A			)
              print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





