
"""simple docstring"""





from __future__ import annotations

from queue import Queue







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   a ^= b
   b ^= a
   a ^= b
   return a, b







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       1
   while (1 << j) < max_node:
      for i in range(1,						max_node + 1    ):
         UpperCAmelCase_   :   int     	=       parent[j - 1][parent[j - 1][i]]
      j += 1
   return parent







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   # u must be deeper in the tree than v
   if level[u] < level[v]:
      UpperCAmelCase_	,  UpperCAmelCase_   :   Dict     	=       swap(__lowerCamelCase,						__lowerCamelCase    )
   # making depth of u same as depth of v
   for i in range(18,						-1,						-1    ):
      if level[u] - (1 << i) >= level[v]:
         UpperCAmelCase_   :   Optional[int]     	=       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(18,						-1,						-1    ):
      if parent[i][u] not in [0, parent[i][v]]:
         UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
   return parent[0][u]







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase = 1,						):
   UpperCAmelCase_   :   Any     	=       0
   UpperCAmelCase_   :   Queue[int]     	=       Queue(maxsize=__lowerCamelCase    )
   q.put(__lowerCamelCase    )
   while q.qsize() != 0:
      UpperCAmelCase_   :   List[str]     	=       q.get()
      for v in graph[u]:
         if level[v] == -1:
            UpperCAmelCase_   :   Dict     	=       level[u] + 1
            q.put(__lowerCamelCase    )
            UpperCAmelCase_   :   Union[str, Any]     	=       u
   return level, parent







def       __a	(						):
   UpperCAmelCase_   :   int     	=       13
   # initializing with 0
   UpperCAmelCase_   :   int     	=       [[0 for _ in range(max_node + 10    )] for _ in range(20    )]
   # initializing with -1 which means every node is unvisited
   UpperCAmelCase_   :   str     	=       [-1 for _ in range(max_node + 10    )]
   UpperCAmelCase_   :   dict[int, list[int]]     	=       {
       1: [2, 3, 4],
       2: [5],
       3: [6, 7],
       4: [8],
       5: [9, 10],
       6: [11],
       7: [],
       8: [12, 13],
       9: [],
       10: [],
       11: [],
       12: [],
       13: [],
   }
   UpperCAmelCase_	,  UpperCAmelCase_   :   int     	=       breadth_first_search(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						1    )
   UpperCAmelCase_   :   List[Any]     	=       create_sparse(__lowerCamelCase,						__lowerCamelCase    )
   print("LCA of node 1 and 3 is: ",						lowest_common_ancestor(1,						3,						__lowerCamelCase,						__lowerCamelCase    )    )
   print("LCA of node 5 and 6 is: ",						lowest_common_ancestor(5,						6,						__lowerCamelCase,						__lowerCamelCase    )    )
   print("LCA of node 7 and 11 is: ",						lowest_common_ancestor(7,						11,						__lowerCamelCase,						__lowerCamelCase    )    )
   print("LCA of node 6 and 7 is: ",						lowest_common_ancestor(6,						7,						__lowerCamelCase,						__lowerCamelCase    )    )
   print("LCA of node 4 and 12 is: ",						lowest_common_ancestor(4,						12,						__lowerCamelCase,						__lowerCamelCase    )    )
   print("LCA of node 8 and 8 is: ",						lowest_common_ancestor(8,						8,						__lowerCamelCase,						__lowerCamelCase    )    )


if __name__ == "__main__":
    main()




"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_a									=				logging.get_logger(__name__)

_a									=				{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Any    	=						SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						"""squeezebert"""







  def __init__(						self							,  lowercase_=3_0522							,  lowercase_=768							,  lowercase_=12							,  lowercase_=12							,  lowercase_=3072							,  lowercase_="gelu"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=512							,  lowercase_=2							,  lowercase_=0.02							,  lowercase_=1E-1_2							,  lowercase_=0							,  lowercase_=768							,  lowercase_=4							,  lowercase_=4							,  lowercase_=4							,  lowercase_=1							,  lowercase_=4							,  lowercase_=4							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_							,  **lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_size
     UpperCAmelCase_   :   Tuple     	=       num_hidden_layers
     UpperCAmelCase_   :   Optional[int]     	=       num_attention_heads
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_act
     UpperCAmelCase_   :   Tuple     	=       intermediate_size
     UpperCAmelCase_   :   Union[str, Any]     	=       hidden_dropout_prob
     UpperCAmelCase_   :   List[Any]     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   int     	=       max_position_embeddings
     UpperCAmelCase_   :   List[str]     	=       type_vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       initializer_range
     UpperCAmelCase_   :   Optional[int]     	=       layer_norm_eps
     UpperCAmelCase_   :   Tuple     	=       embedding_size
     UpperCAmelCase_   :   Dict     	=       q_groups
     UpperCAmelCase_   :   List[Any]     	=       k_groups
     UpperCAmelCase_   :   List[str]     	=       v_groups
     UpperCAmelCase_   :   Optional[Any]     	=       post_attention_groups
     UpperCAmelCase_   :   List[str]     	=       intermediate_groups
     UpperCAmelCase_   :   List[Any]     	=       output_groups




class A_      (lowercase__ ):




  '''simple docstring'''







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_   :   List[str]     	=       {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_   :   Optional[int]     	=       {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]    )


