






class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase = ""       ,    UpperCAmelCase = False    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{}

							# A node will be a leaf if the tree contains its word
							_UpperCAmelCase									=			is_leaf

							_UpperCAmelCase									=			prefix

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			0
							for q, w in zip(self.prefix       ,    UpperCAmelCase    ):
													if q != w:
																			break

													x += 1

							return self.prefix[:x], self.prefix[x:], word[x:]

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							for word in words:
													self.insert(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if self.prefix == word:
													_UpperCAmelCase									=			True

							# Case 2: The node has no edges that have a prefix to the word
							# Solution: We create an edge from the current node to a new one
							# containing the word
							elif word[0] not in self.nodes:
													_UpperCAmelCase									=			RadixNode(prefix=UpperCAmelCase       ,    is_leaf=UpperCAmelCase    )

							else:
													_UpperCAmelCase									=			self.nodes[word[0]]
													_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			incoming_node.match(
													    UpperCAmelCase    )

													# Case 3: The node prefix is equal to the matching
													# Solution: We insert remaining word on the next node
													if remaining_prefix == "":
																			self.nodes[matching_string[0]].insert(UpperCAmelCase    )

													# Case 4: The word is greater equal to the matching
													# Solution: Create a node in between both nodes, change
													# prefixes and add the new node for the remaining word
													else:
																			_UpperCAmelCase									=			remaining_prefix

																			_UpperCAmelCase									=			self.nodes[matching_string[0]]
																			_UpperCAmelCase									=			RadixNode(UpperCAmelCase       ,    UpperCAmelCase    )
																			_UpperCAmelCase									=			aux_node

																			if remaining_word == "":
																									_UpperCAmelCase									=			True
																			else:
																									self.nodes[matching_string[0]].insert(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.nodes.get(word[0]       ,    UpperCAmelCase    )
							if not incoming_node:
													return False
							else:
													_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			incoming_node.match(
													    UpperCAmelCase    )
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																			return False
													# This applies when the word and the prefix are equal
													elif remaining_word == "":
																			return incoming_node.is_leaf
													# We have word remaining so we check the next node
													else:
																			return incoming_node.find(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.nodes.get(word[0]       ,    UpperCAmelCase    )
							if not incoming_node:
													return False
							else:
													_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			incoming_node.match(
													    UpperCAmelCase    )
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																			return False
													# We have word remaining so we check the next node
													elif remaining_word != "":
																			return incoming_node.delete(UpperCAmelCase    )
													else:
																			# If it is not a leaf, we don't have to delete
																			if not incoming_node.is_leaf:
																									return False
																			else:
																									# We delete the nodes if no edges go from it
																									if len(incoming_node.nodes    ) == 0:
																															del self.nodes[word[0]]
																															# We merge the current node with its only child
																															if len(self.nodes    ) == 1 and not self.is_leaf:
																																					_UpperCAmelCase									=			list(self.nodes.values()    )[0]
																																					_UpperCAmelCase									=			merging_node.is_leaf
																																					self.prefix += merging_node.prefix
																																					_UpperCAmelCase									=			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																									elif len(incoming_node.nodes    ) > 1:
																															_UpperCAmelCase									=			False
																									# If there is 1 edge, we merge it with its child
																									else:
																															_UpperCAmelCase									=			list(incoming_node.nodes.values()    )[0]
																															_UpperCAmelCase									=			merging_node.is_leaf
																															incoming_node.prefix += merging_node.prefix
																															_UpperCAmelCase									=			merging_node.nodes

																									return True

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = 0    ):
							"""simple docstring"""







							if self.prefix != "":
													print('-' * height       ,    self.prefix       ,    '  (leaf)' if self.is_leaf else ''    )

							for value in self.nodes.values():
													value.print_tree(height + 1    )



def __A    (      )->  bool:





						"""simple docstring"""



						_UpperCAmelCase									=			'banana bananas bandana band apple all beast'.split()
						_UpperCAmelCase									=			RadixNode()
						root.insert_many(__lowerCAmelCase	)

						assert all(root.find(__lowerCAmelCase	) for word in words	)
						assert not root.find('bandanas'	)
						assert not root.find('apps'	)
						root.delete('all'	)
						assert not root.find('all'	)
						root.delete('banana'	)
						assert not root.find('banana'	)
						assert root.find('bananas'	)

						return True


def __A    (      )->  None:





						"""simple docstring"""



						assert test_trie()


def __A    (      )->  None:





						"""simple docstring"""



						_UpperCAmelCase									=			RadixNode()
						_UpperCAmelCase									=			'banana bananas bandanas bandana band apple all beast'.split()
						root.insert_many(__lowerCAmelCase	)

						print('Words:' ,		__lowerCAmelCase	)
						print('Tree:'	)
						root.print_tree()


if __name__ == "__main__":
		main()




'''simple docstring'''




import unittest

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


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel

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





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





