



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


__A   			=logging.get_logger(__name__)

__A   			={
    '''google/realm-cc-news-pretrained-embedder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-encoder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-scorer''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-openqa''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'''
    ),
    '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json''',
    '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json''',
    # See all REALM models at https://huggingface.co/models?filter=realm
}

class 		UpperCAmelCase__			(			A__   ):




			'''simple docstring'''






			UpperCamelCase									=       """realm"""




			def __init__(   self							: Optional[Any]       ,       a_							: int=3_05_22       ,       a_							: Union[str, Any]=7_68       ,       a_							: Dict=1_28       ,       a_							: Optional[int]=12       ,       a_							: List[str]=12       ,       a_							: Optional[int]=8       ,       a_							: Tuple=30_72       ,       a_							: Optional[int]="gelu_new"       ,       a_							: Tuple=0.1       ,       a_							: Optional[int]=0.1       ,       a_							: Optional[int]=5_12       ,       a_							: Optional[int]=2       ,       a_							: Any=0.0_2       ,       a_							: Tuple=1e-12       ,       a_							: Union[str, Any]=2_56       ,       a_							: int=10       ,       a_							: Tuple=1e-3       ,       a_							: Tuple=5       ,       a_							: List[str]=3_20       ,       a_							: List[Any]=13_35_37_18       ,       a_							: Optional[Any]=50_00       ,       a_							: List[Any]=1       ,       a_							: int=0       ,       a_							: str=2       ,       **a_							: Optional[Any]       ,       ):
					'''simple docstring'''





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

					# Common config
					__UpperCAmelCase    :     Any													=	vocab_size
					__UpperCAmelCase    :     List[Any]													=	max_position_embeddings
					__UpperCAmelCase    :     Any													=	hidden_size
					__UpperCAmelCase    :     Any													=	retriever_proj_size
					__UpperCAmelCase    :     Optional[Any]													=	num_hidden_layers
					__UpperCAmelCase    :     Tuple													=	num_attention_heads
					__UpperCAmelCase    :     str													=	num_candidates
					__UpperCAmelCase    :     Tuple													=	intermediate_size
					__UpperCAmelCase    :     Optional[int]													=	hidden_act
					__UpperCAmelCase    :     Optional[int]													=	hidden_dropout_prob
					__UpperCAmelCase    :     List[str]													=	attention_probs_dropout_prob
					__UpperCAmelCase    :     Dict													=	initializer_range
					__UpperCAmelCase    :     Dict													=	type_vocab_size
					__UpperCAmelCase    :     Union[str, Any]													=	layer_norm_eps

					# Reader config
					__UpperCAmelCase    :     Optional[Any]													=	span_hidden_size
					__UpperCAmelCase    :     Any													=	max_span_width
					__UpperCAmelCase    :     int													=	reader_layer_norm_eps
					__UpperCAmelCase    :     Dict													=	reader_beam_size
					__UpperCAmelCase    :     Any													=	reader_seq_len

					# Retrieval config
					__UpperCAmelCase    :     int													=	num_block_records
					__UpperCAmelCase    :     Tuple													=	searcher_beam_size






from collections import defaultdict




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







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




def       _snake_case	(   ):
		"""simple docstring"""







		dfs(1	)


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