




from __future__ import annotations

from queue import Queue







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->tuple[int, int]:






					"""simple docstring"""






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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->list[list[int]]:






					"""simple docstring"""






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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->int:






					"""simple docstring"""






					if level[u] < level[v]:
										__magic_name__       ,			__magic_name__ :							Tuple		       =							swap(UpperCAmelCase,     UpperCAmelCase	)
					# making depth of u same as depth of v
					for i in range(18,     -1,     -1	):
										if level[u] - (1 << i) >= level[v]:
															__magic_name__ :							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]]:
															__magic_name__       ,			__magic_name__ :							Any		       =							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,     )  ->tuple[list[int], list[list[int]]]:






					"""simple docstring"""






					__magic_name__ :							str		       =							0
					__magic_name__ :							Queue[int]		       =							Queue(maxsize=UpperCAmelCase	)
					q.put(UpperCAmelCase	)
					while q.qsize() != 0:
										__magic_name__ :							Optional[Any]		       =							q.get()
										for v in graph[u]:
															if level[v] == -1:
																				__magic_name__ :							List[str]		       =							level[u] + 1
																				q.put(UpperCAmelCase	)
																				__magic_name__ :							Tuple		       =							u
					return level, parent







def 			lowerCAmelCase  (     )  ->None:






					"""simple docstring"""






					__magic_name__ :							List[Any]		       =							13
					# initializing with 0
					__magic_name__ :							Dict		       =							[[0 for _ in range(max_node + 10	)] for _ in range(20	)]
					# initializing with -1 which means every node is unvisited
					__magic_name__ :							Dict		       =							[-1 for _ in range(max_node + 10	)]
					__magic_name__ :							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: [],
					}
					__magic_name__       ,			__magic_name__ :							int		       =							breadth_first_search(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     1	)
					__magic_name__ :							str		       =							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,     11,     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,     12,     UpperCAmelCase,     UpperCAmelCase	)	)
					print('''LCA of node 8 and 8 is: ''',     lowest_common_ancestor(8,     8,     UpperCAmelCase,     UpperCAmelCase	)	)


if __name__ == "__main__":
				main()









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel





@require_torch
@require_sentencepiece
@require_tokenizers
class 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]    ,					device=lowerCamelCase    ,					dtype=torch.float    ,					)
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]    ,					lowerCamelCase    ,					atol=1e-4						)						)



