from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		{
    "google/tapas-base-finetuned-sqa": (
        "https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-wtq": (
        "https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-wikisql-supervised": (
        "https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-tabfact": (
        "https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
    ),
}
class    A_ (       UpperCamelCase_ ):
  _SCREAMING_SNAKE_CASE    				=			"""tapas"""

  def __init__(		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=3_05_22     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=7_68     ,							__SCREAMING_SNAKE_CASE :      Dict=12     ,							__SCREAMING_SNAKE_CASE :      Any=12     ,							__SCREAMING_SNAKE_CASE :      Dict=30_72     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]="gelu"     ,							__SCREAMING_SNAKE_CASE :      Tuple=0.1     ,							__SCREAMING_SNAKE_CASE :      Tuple=0.1     ,							__SCREAMING_SNAKE_CASE :      str=10_24     ,							__SCREAMING_SNAKE_CASE :      int=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]     ,							__SCREAMING_SNAKE_CASE :      str=0.02     ,							__SCREAMING_SNAKE_CASE :      List[str]=1E-12     ,							__SCREAMING_SNAKE_CASE :      Tuple=0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=10.0     ,							__SCREAMING_SNAKE_CASE :      List[str]=0     ,							__SCREAMING_SNAKE_CASE :      Any=1.0     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      int=1.0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=False     ,							__SCREAMING_SNAKE_CASE :      List[str]=None     ,							__SCREAMING_SNAKE_CASE :      Tuple=1.0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1.0     ,							__SCREAMING_SNAKE_CASE :      List[Any]=False     ,							__SCREAMING_SNAKE_CASE :      str=False     ,							__SCREAMING_SNAKE_CASE :      Any="ratio"     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							__SCREAMING_SNAKE_CASE :      List[str]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=32     ,							__SCREAMING_SNAKE_CASE :      str=False     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      str=False     ,							__SCREAMING_SNAKE_CASE :      Tuple=False     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      List[Any]=False     ,							__SCREAMING_SNAKE_CASE :      List[Any]=None     ,							__SCREAMING_SNAKE_CASE :      List[Any]=None     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
   super().__init__(pad_token_id=_a     ,							**_a       )

   # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_sizes
   __a       =  initializer_range
   __a       =  layer_norm_eps

   # Fine-tuning task hyperparameters
   __a       =  positive_label_weight
   __a       =  num_aggregation_labels
   __a       =  aggregation_loss_weight
   __a       =  use_answer_as_supervision
   __a       =  answer_loss_importance
   __a       =  use_normalized_answer_loss
   __a       =  huber_loss_delta
   __a       =  temperature
   __a       =  aggregation_temperature
   __a       =  use_gumbel_for_cells
   __a       =  use_gumbel_for_aggregation
   __a       =  average_approximation_function
   __a       =  cell_selection_preference
   __a       =  answer_loss_cutoff
   __a       =  max_num_rows
   __a       =  max_num_columns
   __a       =  average_logits_per_cell
   __a       =  select_one_column
   __a       =  allow_empty_column_selection
   __a       =  init_cell_selection_weights_to_zero
   __a       =  reset_position_index_per_cell
   __a       =  disable_per_token_loss

   # Aggregation hyperparameters
   __a       =  aggregation_labels
   __a       =  no_aggregation_label_index

   if isinstance(self.aggregation_labels     ,							_a       ):
    __a       =  {int(_a       ): v for k, v in aggregation_labels.items()}



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   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       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  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       =  _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		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

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



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  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		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


