





'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     __A		:					Optional[int]						=			[1]

     __A					,__A					,__A		:					List[str]						=			0, 0, 0
     __A		:					str						=			ugly_nums[ia] * 2
     __A		:					Optional[int]						=			ugly_nums[ia] * 3
     __A		:					Tuple						=			ugly_nums[ia] * 5

     for _ in range(1					,				__snake_case     ):
          __A		:					List[str]						=			min(__snake_case					,				__snake_case					,				__snake_case     )
          ugly_nums.append(__snake_case     )
          if next_num == next_a:
               ia += 1
               __A		:					Union[str, Any]						=			ugly_nums[ia] * 2
          if next_num == next_a:
               ia += 1
               __A		:					Any						=			ugly_nums[ia] * 3
          if next_num == next_a:
               ia += 1
               __A		:					int						=			ugly_nums[ia] * 5
     return ugly_nums[-1]


if __name__ == "__main__":
  from doctest import testmod

  testmod(verbose=True)
  print(f"""{ugly_numbers(2_00) = }""")






'''simple docstring'''




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


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d