

from __future__ import annotations

from queue import Queue
def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int					)				->							tuple[int, int]:
	a ^= b
	b ^= a
	a ^= b
	return a, b
def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      list[list[int]]					)				->							list[list[int]]:
	__A   : Optional[int]								= 1
	while (1 << j) < max_node:
		for i in range(1 ,max_node + 1					):
			__A   : str								= parent[j - 1][parent[j - 1][i]]
		j += 1
	return parent
def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      list[int] ,a__						:      list[list[int]]					)				->							int:
	# u must be deeper in the tree than v
	if level[u] < level[v]:
		__A						,							__A   : Tuple								= swap(a__ ,a__					)
	# making depth of u same as depth of v
	for i in range(18 ,-1 ,-1					):
		if level[u] - (1 << i) >= level[v]:
			__A   : Tuple								= 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]]:
			__A						,							__A   : Union[str, Any]								= parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
	return parent[0][u]
def        __SCREAMING_SNAKE_CASE    (      a__						:      list[int] ,a__						:      list[list[int]] ,a__						:      int ,a__						:      dict[int, list[int]] ,a__						:      int = 1 ,)				->							tuple[list[int], list[list[int]]]:
	__A   : Tuple								= 0
	__A   : Queue[int]								= Queue(maxsize=a__					)
	q.put(a__					)
	while q.qsize() != 0:
		__A   : Any								= q.get()
		for v in graph[u]:
			if level[v] == -1:
				__A   : Optional[int]								= level[u] + 1
				q.put(a__					)
				__A   : Optional[Any]								= u
	return level, parent
def        __SCREAMING_SNAKE_CASE    (      )				->							None:
	__A   : Any								= 13
	# initializing with 0
	__A   : Dict								= [[0 for _ in range(max_node + 10					)] for _ in range(20					)]
	# initializing with -1 which means every node is unvisited
	__A   : Tuple								= [-1 for _ in range(max_node + 10					)]
	__A   : 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: [],
	}
	__A						,							__A   : Tuple								= breadth_first_search(a__ ,a__ ,a__ ,a__ ,1					)
	__A   : Optional[int]								= create_sparse(a__ ,a__					)
	print("""LCA of node 1 and 3 is: """ ,lowest_common_ancestor(1 ,3 ,a__ ,a__					)					)
	print("""LCA of node 5 and 6 is: """ ,lowest_common_ancestor(5 ,6 ,a__ ,a__					)					)
	print("""LCA of node 7 and 11 is: """ ,lowest_common_ancestor(7 ,11 ,a__ ,a__					)					)
	print("""LCA of node 6 and 7 is: """ ,lowest_common_ancestor(6 ,7 ,a__ ,a__					)					)
	print("""LCA of node 4 and 12 is: """ ,lowest_common_ancestor(4 ,12 ,a__ ,a__					)					)
	print("""LCA of node 8 and 8 is: """ ,lowest_common_ancestor(8 ,8 ,a__ ,a__					)					)


if __name__ == "__main__":
			main()








from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				Any		   =      {
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      str    				=							'''plbart'''
	_lowercase		:      str    				=							['''past_key_values''']
	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
		__A   : List[Any]								= vocab_size
		__A   : str								= max_position_embeddings
		__A   : Union[str, Any]								= d_model
		__A   : List[str]								= encoder_ffn_dim
		__A   : Optional[Any]								= encoder_layers
		__A   : Tuple								= encoder_attention_heads
		__A   : List[Any]								= decoder_ffn_dim
		__A   : Optional[int]								= decoder_layers
		__A   : Any								= decoder_attention_heads
		__A   : Optional[int]								= dropout
		__A   : Any								= attention_dropout
		__A   : Tuple								= activation_dropout
		__A   : List[Any]								= activation_function
		__A   : Tuple								= init_std
		__A   : List[Any]								= encoder_layerdrop
		__A   : str								= decoder_layerdrop
		__A   : Union[str, Any]								= classifier_dropout
		__A   : Optional[Any]								= use_cache
		__A   : List[str]								= encoder_layers
		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
		super().__init__(
		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)






class     lowerCamelCase_			(  _lowercase  ):




	@property
	def 					lowerCAmelCase_					(    self	:    Any       ):
		return OrderedDict(
		    [
		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
		    ]       )




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		if self.use_past:
			return OrderedDict(
			    [
			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			    ]       )
		else:
			return OrderedDict(
			    [
			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			    ]       )





