




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__		     =     logging.get_logger(__name__)

a__		     =     {
    '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
    '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/config.json'''
    # See all FNet models at https://huggingface.co/models?filter=fnet
}


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       List[str]      	=		'fnet'






	def __init__(					self		:						List[Any]					,     lowerCAmelCase		:						Union[str, Any]=32000					,     lowerCAmelCase		:						Optional[int]=768					,     lowerCAmelCase		:						Any=12					,     lowerCAmelCase		:						Tuple=3072					,     lowerCAmelCase		:						List[Any]="gelu_new"					,     lowerCAmelCase		:						Dict=0.1					,     lowerCAmelCase		:						Optional[int]=512					,     lowerCAmelCase		:						str=4					,     lowerCAmelCase		:						List[str]=0.02					,     lowerCAmelCase		:						Optional[Any]=1E-12					,     lowerCAmelCase		:						Optional[int]=False					,     lowerCAmelCase		:						Optional[int]=512					,     lowerCAmelCase		:						Optional[Any]=3					,     lowerCAmelCase		:						Tuple=1					,     lowerCAmelCase		:						int=2					,     **lowerCAmelCase		:						Any					,     )	->       Optional[int]:







						"""simple docstring"""







						super().__init__(pad_token_id=lowerCAmelCase					,     bos_token_id=lowerCAmelCase					,     eos_token_id=lowerCAmelCase					,     **lowerCAmelCase	)

						__UpperCamelCase					:	List[str]             =      vocab_size
						__UpperCamelCase					:	str             =      max_position_embeddings
						__UpperCamelCase					:	str             =      hidden_size
						__UpperCamelCase					:	List[str]             =      num_hidden_layers
						__UpperCamelCase					:	List[str]             =      intermediate_size
						__UpperCamelCase					:	Any             =      hidden_act
						__UpperCamelCase					:	List[Any]             =      hidden_dropout_prob
						__UpperCamelCase					:	List[Any]             =      initializer_range
						__UpperCamelCase					:	Dict             =      type_vocab_size
						__UpperCamelCase					:	str             =      layer_norm_eps
						__UpperCamelCase					:	Tuple             =      use_tpu_fourier_optimizations
						__UpperCamelCase					:	Optional[int]             =      tpu_short_seq_length












from collections import defaultdict





def 				A__ (snake_case   :		int				)     ->						int:
					__UpperCamelCase					:	Optional[int]             =      1
					__UpperCamelCase					:	Tuple             =      True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(snake_case				)
					if ret % 2 == 0:
										cuts.append(snake_case				)
					return ret





def 				A__ ()     ->						str:
					dfs(1				)


if __name__ == "__main__":
	a__     ,							a__		     =     10, 9
	a__		     =     defaultdict(list)
	a__		     =     {}
	a__		     =     []
	a__		     =     0
	a__		     =     [(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)






