




import warnings

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


lowerCamelCase__					:  Tuple      							=				logging.get_logger(__name__)

lowerCamelCase__					:  int      							=				{
    '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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







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



 def __init__(				self    :						str							,							_lowerCAmelCase    :						Optional[int]=32_000							,							_lowerCAmelCase    :						Optional[Any]=1_024							,							_lowerCAmelCase    :						str=24							,							_lowerCAmelCase    :						Any=16							,							_lowerCAmelCase    :						Union[str, Any]=4_096							,							_lowerCAmelCase    :						List[str]="gelu"							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						Optional[int]="bi"							,							_lowerCAmelCase    :						Any=0.02							,							_lowerCAmelCase    :						List[Any]=1E-12							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						List[Any]=512							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						Dict=True							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						str=-1							,							_lowerCAmelCase    :						Dict=False							,							_lowerCAmelCase    :						int="last"							,							_lowerCAmelCase    :						List[Any]=True							,							_lowerCAmelCase    :						Optional[int]="tanh"							,							_lowerCAmelCase    :						Union[str, Any]=0.1							,							_lowerCAmelCase    :						Any=5							,							_lowerCAmelCase    :						List[Any]=5							,							_lowerCAmelCase    :						Optional[int]=5							,							_lowerCAmelCase    :						Optional[int]=1							,							_lowerCAmelCase    :						Dict=2							,							**_lowerCAmelCase    :						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.'							,							_lowerCAmelCase							,							)
             SCREAMING_SNAKE_CASE_      		=     kwargs['use_cache']

       SCREAMING_SNAKE_CASE_      		=     use_mems_eval
       SCREAMING_SNAKE_CASE_      		=     use_mems_train
       super().__init__(pad_token_id=_lowerCAmelCase							,							bos_token_id=_lowerCAmelCase							,							eos_token_id=_lowerCAmelCase							,							**_lowerCAmelCase		)



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, 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  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[int]		):
       # 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."		)





from __future__ import annotations

from collections.abc import Iterator





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     value
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     None





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						int							,							_lowerCAmelCase    :						Node		):
       SCREAMING_SNAKE_CASE_      		=     tree



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Node | None		):
       if node is None:
             return 0
       return node.value + (
           self.depth_first_search(node.left		) + self.depth_first_search(node.right		)
       )




 def __iter__(				self    :						Dict		):
       yield self.depth_first_search(self.tree		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()