



def      __snake_case	(			):
    for n in range(1   , 1000000				):
        yield n * (n + 1) // 2





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				1
    __a						       =				2
    while i * i <= n:
        __a						       =				0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count





def      __snake_case	(			):
    return next(i for i in triangle_number_generator() if count_divisors(_UpperCAmelCase				) > 500				)


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










import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				pruning_method
           __a						       =				mask_init
           __a						       =				mask_scale





