

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowercase     : Dict       =						logging.get_logger(__name__)

__lowercase     : Tuple       =						{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class 						_A     (				snake_case						,    snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      List[str]					=   '''nat'''

  __lowerCamelCase			:      List[Any]					=   {
      '''num_attention_heads''': '''num_heads''',
      '''num_hidden_layers''': '''num_layers''',
  }





  def __init__(  self   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=64   ,SCREAMING_SNAKE_CASE_=[3, 4, 6, 5]   ,SCREAMING_SNAKE_CASE_=[2, 4, 8, 16]   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=3.0   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_="gelu"   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=1E-5   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      super().__init__(**SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		patch_size
      snake_case							:		Union[str, Any]        =		num_channels
      snake_case							:		Dict        =		embed_dim
      snake_case							:		List[str]        =		depths
      snake_case							:		int        =		len(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		num_heads
      snake_case							:		str        =		kernel_size
      snake_case							:		Dict        =		mlp_ratio
      snake_case							:		Any        =		qkv_bias
      snake_case							:		str        =		hidden_dropout_prob
      snake_case							:		str        =		attention_probs_dropout_prob
      snake_case							:		str        =		drop_path_rate
      snake_case							:		Any        =		hidden_act
      snake_case							:		Tuple        =		layer_norm_eps
      snake_case							:		List[Any]        =		initializer_range
      # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      snake_case							:		List[Any]        =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_    ) - 1)    )
      snake_case							:		Any        =		layer_scale_init_value
      snake_case							:		int        =		["""stem"""] + [F"""stage{idx}""" for idx in range(1   ,len(SCREAMING_SNAKE_CASE_    ) + 1    )]
      snake_case      ,		snake_case							:		Union[str, Any]        =		get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE_   ,out_indices=SCREAMING_SNAKE_CASE_   ,stage_names=self.stage_names    )





from __future__ import annotations

lowerCamelCase__						=				"#"







class 							lowerCAmelCase__   :

						def __init__(      self					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						{}

						def 			A_					(      self							,				a					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						self._trie
								for char in text:
										if char not in trie:
												_UpperCamelCase         =						{}
										_UpperCamelCase         =						trie[char]
								_UpperCamelCase         =						True

						def 			A_					(      self							,				a					)       ->   tuple | list:


								'''simple docstring'''

								_UpperCamelCase         =						self._trie
								for char in prefix:
										if char in trie:
												_UpperCamelCase         =						trie[char]
										else:
												return []
								return self._elements(a					)







						def 			A_					(      self							,				a					)       ->   tuple:


								'''simple docstring'''

								_UpperCamelCase         =						[]
								for c, v in d.items():
										_UpperCamelCase         =						[""" """] if c == END else [(c + s) for s in self._elements(a					)]
										result.extend(a					)
								return tuple(a					)




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


def 				__A(lowerCAmelCase			)		->					tuple:

		"""simple docstring"""






		_UpperCamelCase         =						trie.find_word(lowerCAmelCase			)
		return tuple(string + word for word in suffixes			)


def 				__A()		->					None:

		"""simple docstring"""






		print(autocomplete_using_trie("""de"""			)			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()



