


"""simple docstring"""







import warnings

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


a__    :							Dict            =					logging.get_logger(__name__)

a__    :							List[str]            =					{
    '''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 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





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




	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    Optional[int]=3_2_0_0_0      ,      UpperCAmelCase__      :    List[Any]=1_0_2_4      ,      UpperCAmelCase__      :    str=2_4      ,      UpperCAmelCase__      :    Any=1_6      ,      UpperCAmelCase__      :    int=4_0_9_6      ,      UpperCAmelCase__      :    Optional[int]="gelu"      ,      UpperCAmelCase__      :    int=True      ,      UpperCAmelCase__      :    Optional[Any]="bi"      ,      UpperCAmelCase__      :    Optional[int]=0.02      ,      UpperCAmelCase__      :    Optional[Any]=1E-12      ,      UpperCAmelCase__      :    Dict=0.1      ,      UpperCAmelCase__      :    List[str]=5_1_2      ,      UpperCAmelCase__      :    List[Any]=None      ,      UpperCAmelCase__      :    str=True      ,      UpperCAmelCase__      :    Optional[Any]=False      ,      UpperCAmelCase__      :    Optional[int]=False      ,      UpperCAmelCase__      :    List[Any]=-1      ,      UpperCAmelCase__      :    Any=False      ,      UpperCAmelCase__      :    Optional[int]="last"      ,      UpperCAmelCase__      :    Optional[int]=True      ,      UpperCAmelCase__      :    Tuple="tanh"      ,      UpperCAmelCase__      :    Optional[Any]=0.1      ,      UpperCAmelCase__      :    Dict=5      ,      UpperCAmelCase__      :    int=5      ,      UpperCAmelCase__      :    Tuple=5      ,      UpperCAmelCase__      :    str=1      ,      UpperCAmelCase__      :    Dict=2      ,      **UpperCAmelCase__      :    str      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    vocab_size
						__SCREAMING_SNAKE_CASE						       =    d_model
						__SCREAMING_SNAKE_CASE						       =    n_layer
						__SCREAMING_SNAKE_CASE						       =    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})"""       )
						__SCREAMING_SNAKE_CASE						       =    d_model // n_head
						__SCREAMING_SNAKE_CASE						       =    ff_activation
						__SCREAMING_SNAKE_CASE						       =    d_inner
						__SCREAMING_SNAKE_CASE						       =    untie_r
						__SCREAMING_SNAKE_CASE						       =    attn_type

						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    layer_norm_eps

						__SCREAMING_SNAKE_CASE						       =    dropout
						__SCREAMING_SNAKE_CASE						       =    mem_len
						__SCREAMING_SNAKE_CASE						       =    reuse_len
						__SCREAMING_SNAKE_CASE						       =    bi_data
						__SCREAMING_SNAKE_CASE						       =    clamp_len
						__SCREAMING_SNAKE_CASE						       =    same_length

						__SCREAMING_SNAKE_CASE						       =    summary_type
						__SCREAMING_SNAKE_CASE						       =    summary_use_proj
						__SCREAMING_SNAKE_CASE						       =    summary_activation
						__SCREAMING_SNAKE_CASE						       =    summary_last_dropout
						__SCREAMING_SNAKE_CASE						       =    start_n_top
						__SCREAMING_SNAKE_CASE						       =    end_n_top

						__SCREAMING_SNAKE_CASE						       =    bos_token_id
						__SCREAMING_SNAKE_CASE						       =    pad_token_id
						__SCREAMING_SNAKE_CASE						       =    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."      ,      UpperCAmelCase__      ,      )
											__SCREAMING_SNAKE_CASE						       =    kwargs["use_cache"]

						__SCREAMING_SNAKE_CASE						       =    use_mems_eval
						__SCREAMING_SNAKE_CASE						       =    use_mems_train
						super().__init__(pad_token_id=UpperCAmelCase__      ,      bos_token_id=UpperCAmelCase__      ,      eos_token_id=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	@property
	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Optional[Any]:
						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   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    Union[str, Any]       )	->     Optional[Any]:
						# Message copied from Transformer-XL documentation
						raise NotImplementedError(
						    F"""The model {self.model_type} is one of the few models that has no sequence length limit."""       )




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return " ".join(
					    "".join(word[::-1]       ) if len(lowerCAmelCase_       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words('''Hey wollef sroirraw'''))
