def   __A		(			):



 """simple docstring"""


 for n in range(1							,				100_0000					):
  yield n * (n + 1) // 2



def   __A		(			_A					):



 """simple docstring"""


 __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   __A		(			):



 """simple docstring"""


 return next(i for i in triangle_number_generator() if count_divisors(_A					) > 500					)


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



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_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       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


