






"""simple docstring"""


from __future__ import annotations

from queue import Queue



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  a ^= b
  b ^= a
  a ^= b
  return a, b



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					1
  while (1 << j) < max_node:
    for i in range(1						,			max_node + 1					):
      __A		     =					parent[j - 1][parent[j - 1][i]]
    j += 1
  return parent



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if level[u] < level[v]:
    __A     ,       __A		     =					swap(__UpperCamelCase						,			__UpperCamelCase					)
  # making depth of u same as depth of v
  for i in range(1_8						,			-1						,			-1					):
    if level[u] - (1 << i) >= level[v]:
      __A		     =					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]]:
      __A     ,       __A		     =					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
  return parent[0][u]



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase = 1						,			):




  """simple docstring"""







  __A		     =					0
  __A		     =					Queue(maxsize=__UpperCamelCase					)
  q.put(__UpperCamelCase					)
  while q.qsize() != 0:
    __A		     =					q.get()
    for v in graph[u]:
      if level[v] == -1:
        __A		     =					level[u] + 1
        q.put(__UpperCamelCase					)
        __A		     =					u
  return level, parent



def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					1_3
  # initializing with 0
  __A		     =					[[0 for _ in range(max_node + 1_0					)] for _ in range(2_0					)]
  # initializing with -1 which means every node is unvisited
  __A		     =					[-1 for _ in range(max_node + 1_0					)]
  __A		     =					{
      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: [],
  }
  __A     ,       __A		     =					breadth_first_search(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			1					)
  __A		     =					create_sparse(__UpperCamelCase						,			__UpperCamelCase					)
  print('''LCA of node 1 and 3 is: '''						,			lowest_common_ancestor(1						,			3						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 5 and 6 is: '''						,			lowest_common_ancestor(5						,			6						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 7 and 11 is: '''						,			lowest_common_ancestor(7						,			1_1						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 6 and 7 is: '''						,			lowest_common_ancestor(6						,			7						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 4 and 12 is: '''						,			lowest_common_ancestor(4						,			1_2						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 8 and 8 is: '''						,			lowest_common_ancestor(8						,			8						,			__UpperCamelCase						,			__UpperCamelCase					)					)


if __name__ == "__main__":
       main()












"""simple docstring"""


from typing import List, Union

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


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"led"
   A_		:      Any							=					{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }


   def __init__(      self					:	Dict,			_lowerCamelCase					:	str=5_02_65,			_lowerCamelCase					:	List[Any]=1_63_84,			_lowerCamelCase					:	int=10_24,			_lowerCamelCase					:	List[Any]=12,			_lowerCamelCase					:	int=40_96,			_lowerCamelCase					:	str=16,			_lowerCamelCase					:	Dict=12,			_lowerCamelCase					:	Optional[int]=40_96,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	int=0.0,			_lowerCamelCase					:	str=0.0,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Tuple="gelu",			_lowerCamelCase					:	Tuple=10_24,			_lowerCamelCase					:	Dict=0.1,			_lowerCamelCase					:	Tuple=0.0,			_lowerCamelCase					:	Union[str, Any]=0.0,			_lowerCamelCase					:	Optional[int]=0.02,			_lowerCamelCase					:	List[str]=2,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Dict=1,			_lowerCamelCase					:	str=0,			_lowerCamelCase					:	Union[str, Any]=2,			_lowerCamelCase					:	Union[List[int], int] = 5_12,			**_lowerCamelCase					:	Any,			):

     '''simple docstring'''
     __A		     =					vocab_size
     __A		     =					max_encoder_position_embeddings
     __A		     =					max_decoder_position_embeddings
     __A		     =					d_model
     __A		     =					encoder_ffn_dim
     __A		     =					encoder_layers
     __A		     =					encoder_attention_heads
     __A		     =					decoder_ffn_dim
     __A		     =					decoder_layers
     __A		     =					decoder_attention_heads
     __A		     =					dropout
     __A		     =					attention_dropout
     __A		     =					activation_dropout
     __A		     =					activation_function
     __A		     =					init_std
     __A		     =					encoder_layerdrop
     __A		     =					decoder_layerdrop
     __A		     =					classifier_dropout
     __A		     =					use_cache
     __A		     =					encoder_layers
     __A		     =					attention_window

     super().__init__(
         pad_token_id=_lowerCamelCase,			bos_token_id=_lowerCamelCase,			eos_token_id=_lowerCamelCase,			is_encoder_decoder=_lowerCamelCase,			decoder_start_token_id=_lowerCamelCase,			**_lowerCamelCase,			)




