




'''simple docstring'''



class      lowerCAmelCase :


       def __init__( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	{}  # Mapping from char to TrieNode
            __snake_case            =	False


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	List[str]:



            '''simple docstring'''




            for word in words:
                 self.insert(__lowerCamelCase	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self
            for char in word:
                 if char not in curr.nodes:
                      __snake_case            =	TrieNode()
                 __snake_case            =	curr.nodes[char]
            __snake_case            =	True


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self
            for char in word:
                 if char not in curr.nodes:
                      return False
                 __snake_case            =	curr.nodes[char]
            return curr.is_leaf




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




            def _delete(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) -> bool:
                 if index == len(__lowerCamelCase	):
                      # If word does not exist
                      if not curr.is_leaf:
                           return False
                      __snake_case            =	False
                      return len(curr.nodes	) == 0
                 __snake_case            =	word[index]
                 __snake_case            =	curr.nodes.get(__lowerCamelCase	)
                 # If char not in current trie node
                 if not char_node:
                      return False
                 # Flag to check if node can be deleted
                 __snake_case            =	_delete(__lowerCamelCase     ,	__lowerCamelCase     ,	index + 1	)
                 if delete_curr:
                      del curr.nodes[char]
                      return len(curr.nodes	) == 0
                 return delete_curr

            _delete(self     ,	__lowerCamelCase     ,	0	)







def       _UpperCamelCase			(_lowerCamelCase				:  TrieNode ,						_lowerCamelCase				:  str						)-> List[Any]:




     '''simple docstring'''





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

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







def       _UpperCamelCase			()-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	'''banana bananas bandana band apple all beast'''.split()
     __snake_case            =	TrieNode()
     root.insert_many(UpperCamelCase_						)
     # print_words(root, "")
     assert all(root.find(UpperCamelCase_						) 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       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  bool						)-> Optional[int]:




     '''simple docstring'''





     print(str(UpperCamelCase_						) ,						'''works!''' if passes else '''doesn\'t work :('''						)







def       _UpperCamelCase			()-> Union[str, Any]:




     '''simple docstring'''





     assert test_trie()







def       _UpperCamelCase			()-> Any:




     '''simple docstring'''





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


if __name__ == "__main__":
     main()










'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            for _ in range(2 )
        ] )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

    UpperCAmelCase__	:Dict      =      []
    UpperCAmelCase__	:Tuple      =      0
    # attention_mask is not used yet
    for i in range(2 ):
       # for each of the two transformers, pass the corresponding condition tokens
       UpperCAmelCase__	:List[Any]      =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       UpperCAmelCase__	:Dict      =      self.transformer_index_for_condition[i]
       UpperCAmelCase__	:Tuple      =      self.transformers[transformer_index](
           __lowerCamelCase			,       encoder_hidden_states=__lowerCamelCase			,       timestep=__lowerCamelCase			,       cross_attention_kwargs=__lowerCamelCase			,       return_dict=__lowerCamelCase			,       )[0]
       encoded_states.append(encoded_state - input_states )
       tokens_start += self.condition_lengths[i]

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



