



'''simple docstring'''





from collections import defaultdict




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						1
     _a											=						True
     for v in tree[start]:
          if v not in visited:
               ret += dfs(__A)
     if ret % 2 == 0:
          cuts.append(__A)
     return ret




def lowerCAmelCase						():



     """simple docstring"""



     dfs(1)


if __name__ == "__main__":
  lowercase_ ,      lowercase_	 =						10, 9
  lowercase_	 =						defaultdict(list)
  lowercase_	 =						{}
  lowercase_	 =						[]
  lowercase_	 =						0
  lowercase_	 =						[(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 ....configuration_utils import PretrainedConfig
from ....utils import logging


lowercase_	 =						logging.get_logger(__name__)

# TODO: upload to AWS
lowercase_	 =						{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[Any]    = 'retribert'



 def __init__(self				,     A=30_522				,     A=768				,     A=8				,     A=12				,     A=3_072				,     A="gelu"				,     A=0.1				,     A=0.1				,     A=512				,     A=2				,     A=0.02				,     A=1E-12				,     A=True				,     A=128				,     A=0				,     **A				,     )					->  Any:


      """simple docstring"""



      super().__init__(pad_token_id=A				,     **A )

      _a											=						vocab_size
      _a											=						hidden_size
      _a											=						num_hidden_layers
      _a											=						num_attention_heads
      _a											=						hidden_act
      _a											=						intermediate_size
      _a											=						hidden_dropout_prob
      _a											=						attention_probs_dropout_prob
      _a											=						max_position_embeddings
      _a											=						type_vocab_size
      _a											=						initializer_range
      _a											=						layer_norm_eps
      _a											=						share_encoders
      _a											=						projection_dim



