





class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					List[str]     )    ->				None:
         A						:     dict[str, TrieNode]         ={}  # Mapping from char to TrieNode
         A						:     Optional[int]         =False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					list[str]     )    ->				None:

         for word in words:
                self.insert(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				None:
         A						:     Optional[int]         =self
         for char in word:
                if char not in curr.nodes:
                       A						:     Union[str, Any]         =TrieNode()
                A						:     List[Any]         =curr.nodes[char]
         A						:     Union[str, Any]         =True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				bool:
         A						:     List[Any]         =self
         for char in word:
                if char not in curr.nodes:
                       return False
                A						:     Tuple         =curr.nodes[char]
         return curr.is_leaf







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				None:

         def _delete(SCREAMING_SNAKE_CASE__    :					TrieNode      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int     ) -> bool:
                if index == len(SCREAMING_SNAKE_CASE__     ):
                       # If word does not exist
                       if not curr.is_leaf:
                              return False
                       A						:     Union[str, Any]         =False
                       return len(curr.nodes     ) == 0
                A						:     Dict         =word[index]
                A						:     Dict         =curr.nodes.get(SCREAMING_SNAKE_CASE__     )
                # If char not in current trie node
                if not char_node:
                       return False
                # Flag to check if node can be deleted
                A						:     Optional[int]         =_delete(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	index + 1     )
                if delete_curr:
                       del curr.nodes[char]
                       return len(curr.nodes     ) == 0
                return delete_curr

         _delete(self      ,	SCREAMING_SNAKE_CASE__      ,	0     )







def A__  (			lowercase:     TrieNode,		lowercase:     str					)				->		None:




       if node.is_leaf:
              print(__A,		end=' '					)

       for key, value in node.nodes.items():
              print_words(__A,		word + key					)



def A__  (			)				->		bool:
       A						:     Union[str, Any]         ='''banana bananas bandana band apple all beast'''.split()
       A						:     Dict         =TrieNode()
       root.insert_many(__A					)
       # print_words(root, "")
       assert all(root.find(__A					) for word in words					)
       assert root.find('banana'					)
       assert not root.find('bandanas'					)
       assert not root.find('apps'					)
       assert root.find('apple'					)
       assert root.find('all'					)
       root.delete('all'					)
       assert not root.find('all'					)
       root.delete('banana'					)
       assert not root.find('banana'					)
       assert root.find('bananas'					)
       return True



def A__  (			lowercase:     str,		lowercase:     bool					)				->		None:
       print(str(__A					),		'works!' if passes else 'doesn\'t work :('					)



def A__  (			)				->		None:
       assert test_trie()



def A__  (			)				->		None:
       print_results('Testing trie functionality',		test_trie()					)


if __name__ == "__main__":
    main()







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


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






