



"""simple docstring"""





from collections import defaultdict
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int     ):


						'''simple docstring'''






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


						'''simple docstring'''






						dfs(1     )


if __name__ == "__main__":
					__lowerCAmelCase       ,			__lowerCAmelCase							:       List[Any]          =       10, 9
					__lowerCAmelCase							:       List[str]          =       defaultdict(list)
					__lowerCAmelCase							:       dict[int, bool]          =       {}
					__lowerCAmelCase							:       list[int]          =       []
					__lowerCAmelCase							:       int          =       0
					__lowerCAmelCase							:       Optional[int]          =       [(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)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

