





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A_		:  List[str]		 =						logging.get_logger(__name__)

A_		:  Optional[Any]		 =						{
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}
class 					__snake_case  (					__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''swin'''

   lowerCamelCase__    							=      {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }

   def __init__(							self	,		__SCREAMING_SNAKE_CASE=2_2_4	,		__SCREAMING_SNAKE_CASE=4	,		__SCREAMING_SNAKE_CASE=3	,		__SCREAMING_SNAKE_CASE=9_6	,		__SCREAMING_SNAKE_CASE=[2, 2, 6, 2]	,		__SCREAMING_SNAKE_CASE=[3, 6, 1_2, 2_4]	,		__SCREAMING_SNAKE_CASE=7	,		__SCREAMING_SNAKE_CASE=4.0	,		__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=False	,		__SCREAMING_SNAKE_CASE=0.02	,		__SCREAMING_SNAKE_CASE=1e-5	,		__SCREAMING_SNAKE_CASE=3_2	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		**__SCREAMING_SNAKE_CASE	,		):
    super().__init__(**__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[str]    =  image_size
    snake_case__     :					Dict    =  patch_size
    snake_case__     :					Union[str, Any]    =  num_channels
    snake_case__     :					List[str]    =  embed_dim
    snake_case__     :					Optional[Any]    =  depths
    snake_case__     :					Dict    =  len(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  num_heads
    snake_case__     :					str    =  window_size
    snake_case__     :					Union[str, Any]    =  mlp_ratio
    snake_case__     :					List[str]    =  qkv_bias
    snake_case__     :					List[str]    =  hidden_dropout_prob
    snake_case__     :					Optional[Any]    =  attention_probs_dropout_prob
    snake_case__     :					Tuple    =  drop_path_rate
    snake_case__     :					Tuple    =  hidden_act
    snake_case__     :					Union[str, Any]    =  use_absolute_embeddings
    snake_case__     :					int    =  layer_norm_eps
    snake_case__     :					List[str]    =  initializer_range
    snake_case__     :					Optional[Any]    =  encoder_stride
    # 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
    snake_case__     :					Optional[Any]    =  int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE       ) - 1)       )
    snake_case__     :					Tuple    =  ["""stem"""] + [f"stage{idx}" for idx in range(1	,		len(__SCREAMING_SNAKE_CASE       ) + 1       )]
    snake_case__  ,							snake_case__     :					List[Any]    =  get_aligned_output_features_output_indices(
        out_features=__SCREAMING_SNAKE_CASE	,		out_indices=__SCREAMING_SNAKE_CASE	,		stage_names=self.stage_names       )



class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      version.parse('''1.11'''							)

   @property
   def 		__UpperCamelCase						(							self       ):
    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]       )


   @property
   def 		__UpperCamelCase						(							self       ):
    return 1e-4








"""simple docstring"""




from collections import defaultdict





def       a_ (	lowercase__ :int     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      True
			for v in tree[start]:
						if v not in visited:
									ret += dfs(lowercase__     )
			if ret % 2 == 0:
						cuts.append(lowercase__     )
			return ret





def       a_ (	):
			dfs(1     )


if __name__ == "__main__":
					__magic_name__  , __magic_name__						: Tuple    =				1_0, 9
					__magic_name__						: Tuple    =				defaultdict(list)
					__magic_name__						: dict[int, bool]    =				{}
					__magic_name__						: list[int]    =				[]
					__magic_name__						: List[str]    =				0
					__magic_name__						: Tuple    =				[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)




