


"""simple docstring"""







import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}


class       SCREAMING_SNAKE_CASE__				(		_lowerCamelCase				):
       """simple docstring"""


       a  :				Optional[int]   ="xlnet"
       a  :				Tuple   =["mems"]
       a  :				Tuple   ={
           "n_token": "vocab_size",  # Backward compatibility
           "hidden_size": "d_model",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=1_024		,     snake_case__=24		,     snake_case__=16		,     snake_case__=4_096		,     snake_case__="gelu"		,     snake_case__=True		,     snake_case__="bi"		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=0.1		,     snake_case__=512		,     snake_case__=None		,     snake_case__=True		,     snake_case__=False		,     snake_case__=False		,     snake_case__=-1		,     snake_case__=False		,     snake_case__="last"		,     snake_case__=True		,     snake_case__="tanh"		,     snake_case__=0.1		,     snake_case__=5		,     snake_case__=5		,     snake_case__=5		,     snake_case__=1		,     snake_case__=2		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							vocab_size
         lowerCAmelCase     :     List[Any]								=							d_model
         lowerCAmelCase     :     Optional[Any]								=							n_layer
         lowerCAmelCase     :     int								=							n_head
         if d_model % n_head != 0:
           raise ValueError(f"""\'d_model % n_head\' ({d_model % n_head}) should be equal to 0"""						)
         if "d_head" in kwargs:
           if kwargs["d_head"] != d_model // n_head:
             raise ValueError(
                 f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""						)
         lowerCAmelCase     :     List[Any]								=							d_model // n_head
         lowerCAmelCase     :     str								=							ff_activation
         lowerCAmelCase     :     Tuple								=							d_inner
         lowerCAmelCase     :     List[str]								=							untie_r
         lowerCAmelCase     :     Any								=							attn_type

         lowerCAmelCase     :     List[str]								=							initializer_range
         lowerCAmelCase     :     Optional[Any]								=							layer_norm_eps

         lowerCAmelCase     :     List[Any]								=							dropout
         lowerCAmelCase     :     List[str]								=							mem_len
         lowerCAmelCase     :     Optional[int]								=							reuse_len
         lowerCAmelCase     :     Any								=							bi_data
         lowerCAmelCase     :     int								=							clamp_len
         lowerCAmelCase     :     Optional[Any]								=							same_length

         lowerCAmelCase     :     Union[str, Any]								=							summary_type
         lowerCAmelCase     :     Union[str, Any]								=							summary_use_proj
         lowerCAmelCase     :     List[str]								=							summary_activation
         lowerCAmelCase     :     Dict								=							summary_last_dropout
         lowerCAmelCase     :     Optional[Any]								=							start_n_top
         lowerCAmelCase     :     Optional[Any]								=							end_n_top

         lowerCAmelCase     :     Tuple								=							bos_token_id
         lowerCAmelCase     :     Tuple								=							pad_token_id
         lowerCAmelCase     :     Dict								=							eos_token_id

         if "use_cache" in kwargs:
           warnings.warn(
               "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
               " instead."		,     lowerCAmelCase_		,     )
           lowerCAmelCase     :     int								=							kwargs["use_cache"]

         lowerCAmelCase     :     Tuple								=							use_mems_eval
         lowerCAmelCase     :     Union[str, Any]								=							use_mems_train
         super().__init__(pad_token_id=lowerCAmelCase_		,     bos_token_id=lowerCAmelCase_		,     eos_token_id=lowerCAmelCase_		,     **lowerCAmelCase_						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)
         return -1







       @max_position_embeddings.setter
       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         raise NotImplementedError(
             f"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)







'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	_snake_case   						=		1
	_snake_case   						=		2
	while i * i <= n:
		_snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   )       ->	List[str]:
	_snake_case   						=		1
	_snake_case   						=		1

	while True:
		i += 1
		t_num += i

		if count_divisors(__A	) > 500:
			break

	return t_num


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

