
'''simple docstring'''







class 						__UpperCAmelCase      :


							def __init__(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		val
								_snake_case   						=		None
								_snake_case   						=		None




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if self.val:
									if val < self.val:
										if self.left is None:
											_snake_case   						=		Node(lowerCAmelCase_  )
										else:
											self.left.insert(lowerCAmelCase_  )
									elif val > self.val:
										if self.right is None:
											_snake_case   						=		Node(lowerCAmelCase_  )
										else:
											self.right.insert(lowerCAmelCase_  )
								else:
									_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	List[Any]:
	# Recursive traversal
	if root:
		inorder(root.left			,				__A	)
		res.append(root.val	)
		inorder(root.right			,				__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Union[str, Any]:
	# Build BST
	if len(__A	) == 0:
		return arr
	_snake_case   						=		Node(arr[0]	)
	for i in range(1			,				len(__A	)	):
		root.insert(arr[i]	)
	# Traverse BST in order.
	_snake_case   						=		[]
	inorder(__A			,				__A	)
	return res


if __name__ == "__main__":
					print(tree_sort([10, 1, 3, 2, 9, 14, 13]))



'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFCamembertModel.from_pretrained('jplu/tf-camembert-base'  )

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,   dtype=tf.floataa    ,   )
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,   expected_slice.numpy()    ,   atol=1E-4  )  )

