

'''simple docstring'''







def    UpperCAmelCase__     (   )      ->      Union[str, Any]:
       for n in range(1,							1_00_00_00					):
              yield n * (n + 1) // 2
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Any:
       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    UpperCAmelCase__     (   )      ->      Union[str, Any]:
       return next(i for i in triangle_number_generator() if count_divisors(UpperCAmelCase__					) > 5_00					)


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



'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           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_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
