
'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	1
						lowercase_     :					int													=	2
						while i * i <= n:
												lowercase_     :					List[Any]													=	0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												n_divisors *= multiplicity + 1
												i += 1
						if n > 1:
												n_divisors *= 2
						return n_divisors
def  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Any													=	1
						lowercase_     :					Any													=	1

						while True:
												i += 1
												t_num += i

												if count_divisors(__SCREAMING_SNAKE_CASE							) > 500:
																		break

						return t_num


if __name__ == "__main__":
							print(solution())





'''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			:  Tuple   							=	logging.getLogger(__name__)






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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	super().to_dict()
											for k, v in d.items():
																	if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																							lowercase_     :					str													=	v.to_dict()
											return d



