


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


A      :    str		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__							,  lowercase__):


       """simple docstring"""




       A__											=			"maskformer-swin"

       A__											=			{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }





       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Optional[Any]=224					,      __lowerCamelCase							:			List[Any]=4					,      __lowerCamelCase							:			Optional[int]=3					,      __lowerCamelCase							:			Dict=96					,      __lowerCamelCase							:			List[Any]=[2, 2, 6, 2]					,      __lowerCamelCase							:			Optional[int]=[3, 6, 12, 24]					,      __lowerCamelCase							:			int=7					,      __lowerCamelCase							:			Optional[Any]=4.0					,      __lowerCamelCase							:			Union[str, Any]=True					,      __lowerCamelCase							:			List[str]=0.0					,      __lowerCamelCase							:			int=0.0					,      __lowerCamelCase							:			List[Any]=0.1					,      __lowerCamelCase							:			Any="gelu"					,      __lowerCamelCase							:			str=False					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			List[Any]=1E-5					,      __lowerCamelCase							:			Optional[Any]=None					,      __lowerCamelCase							:			Any=None					,      **__lowerCamelCase							:			Dict					,      ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase       )

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






from collections import defaultdict

def       lowercase_		(		_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				Union[str, Any]    =  1
      lowerCamelCase__		:				Dict    =  True
      for v in tree[start]:
            if v not in visited:
                  ret += dfs(_A						)
      if ret % 2 == 0:
            cuts.append(_A						)
      return ret

def       lowercase_		(		):

      """simple docstring"""




      dfs(1						)


if __name__ == "__main__":
    A,  A      :    Tuple		=					10, 9
    A      :    int		=					defaultdict(list)
    A      :    dict[int, bool]		=					{}
    A      :    list[int]		=					[]
    A      :    List[str]		=					0
    A      :    Tuple		=					[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
    for u, v in edges:
        tree[u].append(v)
        tree[v].append(u)
    even_tree()
    print(len(cuts) - 1)


