from __future__ import annotations

from queue import Queue







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    a ^= b
    b ^= a
    a ^= b
    return a, b







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					list[list[int]]   ):







    '''simple docstring'''
    __snake_case       :				str 			=    1
    while (1 << j) < max_node:
        for i in range(1		,							max_node + 1   ):
            __snake_case       :				Dict 			=    parent[j - 1][parent[j - 1][i]]
        j += 1
    return parent







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					list[int]		,							__SCREAMING_SNAKE_CASE       :					list[list[int]]   ):







    '''simple docstring'''
    # u must be deeper in the tree than v
    if level[u] < level[v]:
        __snake_case			, __snake_case       :				Tuple 			=    swap(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
    # making depth of u same as depth of v
    for i in range(1_8		,							-1		,							-1   ):
        if level[u] - (1 << i) >= level[v]:
            __snake_case       :				Any 			=    parent[i][u]
    # at the same depth if u==v that mean lca is found
    if u == v:
        return u
    # moving both nodes upwards till lca in found
    for i in range(1_8		,							-1		,							-1   ):
        if parent[i][u] not in [0, parent[i][v]]:
            __snake_case			, __snake_case       :				str 			=    parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
    return parent[0][u]







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[int]		,							__SCREAMING_SNAKE_CASE       :					list[list[int]]		,							__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					dict[int, list[int]]		,							__SCREAMING_SNAKE_CASE       :					int = 1		,							):







    '''simple docstring'''
    __snake_case       :				Tuple 			=    0
    __snake_case       :				Queue[int] 			=    Queue(maxsize=__SCREAMING_SNAKE_CASE   )
    q.put(__SCREAMING_SNAKE_CASE   )
    while q.qsize() != 0:
        __snake_case       :				List[str] 			=    q.get()
        for v in graph[u]:
            if level[v] == -1:
                __snake_case       :				Union[str, Any] 			=    level[u] + 1
                q.put(__SCREAMING_SNAKE_CASE   )
                __snake_case       :				List[Any] 			=    u
    return level, parent







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    __snake_case       :				List[str] 			=    1_3
    # initializing with 0
    __snake_case       :				Optional[Any] 			=    [[0 for _ in range(max_node + 1_0   )] for _ in range(2_0   )]
    # initializing with -1 which means every node is unvisited
    __snake_case       :				Optional[int] 			=    [-1 for _ in range(max_node + 1_0   )]
    __snake_case       :				dict[int, list[int]] 			=    {
        1: [2, 3, 4],
        2: [5],
        3: [6, 7],
        4: [8],
        5: [9, 1_0],
        6: [1_1],
        7: [],
        8: [1_2, 1_3],
        9: [],
        1_0: [],
        1_1: [],
        1_2: [],
        1_3: [],
    }
    __snake_case			, __snake_case       :				Union[str, Any] 			=    breadth_first_search(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							1   )
    __snake_case       :				List[Any] 			=    create_sparse(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
    print("""LCA of node 1 and 3 is: """		,							lowest_common_ancestor(1		,							3		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )   )
    print("""LCA of node 5 and 6 is: """		,							lowest_common_ancestor(5		,							6		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )   )
    print("""LCA of node 7 and 11 is: """		,							lowest_common_ancestor(7		,							1_1		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )   )
    print("""LCA of node 6 and 7 is: """		,							lowest_common_ancestor(6		,							7		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )   )
    print("""LCA of node 4 and 12 is: """		,							lowest_common_ancestor(4		,							1_2		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )   )
    print("""LCA of node 8 and 8 is: """		,							lowest_common_ancestor(8		,							8		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )   )


if __name__ == "__main__":
     main()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



