

'''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 					lowercase						(  unittest.TestCase ):


							@slow
							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					XLMRobertaModel.from_pretrained('xlm-roberta-base')
								UpperCAmelCase_		:      List[str]       	=					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
								# The dog is cute and lives in the garden house

								UpperCAmelCase_		:      Optional[Any]       	=					torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase_		:      Optional[int]       	=					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():
									UpperCAmelCase_		:      Union[str, Any]       	=					model(_SCREAMING_SNAKE_CASE)['last_hidden_state'].detach()
								self.assertEqual(output.shape    ,			_SCREAMING_SNAKE_CASE)
								# compare the actual values for a slice of last dim
								self.assertTrue(torch.allclose(output[:, :, -1]    ,			_SCREAMING_SNAKE_CASE    ,			atol=1e-3))





							@slow
							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					XLMRobertaModel.from_pretrained('xlm-roberta-large')
								UpperCAmelCase_		:      Dict       	=					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
								# The dog is cute and lives in the garden house

								UpperCAmelCase_		:      Optional[Any]       	=					torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase_		:      int       	=					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():
									UpperCAmelCase_		:      Optional[Any]       	=					model(_SCREAMING_SNAKE_CASE)['last_hidden_state'].detach()
								self.assertEqual(output.shape    ,			_SCREAMING_SNAKE_CASE)
								# compare the actual values for a slice of last dim
								self.assertTrue(torch.allclose(output[:, :, -1]    ,			_SCREAMING_SNAKE_CASE    ,			atol=1e-3))









'''simple docstring'''


from __future__ import annotations

from typing import Any



class 					lowercase						(  a_ ):
							pass



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Any       	=					data
								UpperCAmelCase_		:      Node | None       	=					None


							def __iter__(			self)							->				Optional[int]:
								UpperCAmelCase_		:      int       	=					self
								UpperCAmelCase_		:      List[str]       	=					[]
								while node:
									if node in visited:
										raise ContainsLoopError
									visited.append(_snake_case)
									yield node.data
									UpperCAmelCase_		:      Tuple       	=					node.next_node





							@property
							def 						_snake_case							(			self)							->				bool:
								try:
									list(self)
									return False
								except ContainsLoopError:
									return True


if __name__ == "__main__":
						lowerCAmelCase__										= Node(1)
						lowerCAmelCase__										= Node(2)
						lowerCAmelCase__										= Node(3)
						lowerCAmelCase__										= Node(4)
						print(root_node.has_loop)  # False
						lowerCAmelCase__										= root_node.next_node
						print(root_node.has_loop)  # True

						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						print(root_node.has_loop)  # False

						lowerCAmelCase__										= Node(1)
						print(root_node.has_loop)  # False






