'''simple docstring'''
from __future__ import annotations

a=  '''#'''






class 			__lowercase		:







     def __init__(		self    ):
         __UpperCamelCase	:	dict		    =  {}







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  self._trie
         for char in text:
             if char not in trie:
                 __UpperCamelCase	:	str		    =  {}
             __UpperCamelCase	:	Optional[Any]		    =  trie[char]
         __UpperCamelCase	:	str		    =  True







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Tuple		    =  self._trie
         for char in prefix:
             if char in trie:
                 __UpperCamelCase	:	int		    =  trie[char]
             else:
                 return []
         return self._elements(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  []
         for c, v in d.items():
             __UpperCamelCase	:	Any		    =  [' '] if c == END else [(c + s) for s in self._elements(_lowerCamelCase    )]
             result.extend(_lowerCamelCase    )
         return tuple(_lowerCamelCase    )




a=  Trie()
a=  ('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
  trie.insert_word(word)







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  trie.find_word(_a			)
    return tuple(string + word for word in suffixes			)







def  _UpperCamelCase (     ):




    """simple docstring"""



    print(autocomplete_using_trie('de'			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




'''simple docstring'''
import warnings

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


a=  logging.get_logger(__name__)

a=  {
    '''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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







     def __init__(		self     ,			_lowerCamelCase=3_2_0_0_0     ,			_lowerCamelCase=1_0_2_4     ,			_lowerCamelCase=2_4     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=4_0_9_6     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="bi"     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-1_2     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=None     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=-1     ,			_lowerCamelCase=False     ,			_lowerCamelCase="last"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="tanh"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[str]		    =  vocab_size
         __UpperCamelCase	:	List[Any]		    =  d_model
         __UpperCamelCase	:	List[str]		    =  n_layer
         __UpperCamelCase	:	Optional[Any]		    =  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})"""    )
         __UpperCamelCase	:	str		    =  d_model // n_head
         __UpperCamelCase	:	Union[str, Any]		    =  ff_activation
         __UpperCamelCase	:	str		    =  d_inner
         __UpperCamelCase	:	Any		    =  untie_r
         __UpperCamelCase	:	List[Any]		    =  attn_type

         __UpperCamelCase	:	str		    =  initializer_range
         __UpperCamelCase	:	Optional[int]		    =  layer_norm_eps

         __UpperCamelCase	:	Union[str, Any]		    =  dropout
         __UpperCamelCase	:	List[str]		    =  mem_len
         __UpperCamelCase	:	Optional[int]		    =  reuse_len
         __UpperCamelCase	:	str		    =  bi_data
         __UpperCamelCase	:	Tuple		    =  clamp_len
         __UpperCamelCase	:	List[Any]		    =  same_length

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

         __UpperCamelCase	:	Optional[int]		    =  bos_token_id
         __UpperCamelCase	:	Optional[Any]		    =  pad_token_id
         __UpperCamelCase	:	List[Any]		    =  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     ,			)
             __UpperCamelCase	:	Optional[int]		    =  kwargs['use_cache']

         __UpperCamelCase	:	Optional[int]		    =  use_mems_eval
         __UpperCamelCase	:	Tuple		    =  use_mems_train
         super().__init__(pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase     ,			**_lowerCamelCase    )







     @property
     def lowerCAmelCase     (		self    ):
         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     ,			_lowerCamelCase    ):
         # 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."""    )



