





'''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 					__a      (unittest.TestCase     ):

							@slow
							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  XLMRobertaModel.from_pretrained('''xlm-roberta-base'''   )
													UpperCAmelCase_			:      List[Any]     						=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
													# The dog is cute and lives in the garden house

													UpperCAmelCase_			:      int     						=  torch.Size((1, 12, 7_68)   )  # batch_size, sequence_length, embedding_vector_dim
													UpperCAmelCase_			:      Tuple     						=  torch.tensor(
													    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]   )
													#  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_			:      Optional[int]     						=  model(__magic_name__   )['''last_hidden_state'''].detach()
													self.assertEqual(output.shape       ,     __magic_name__   )
													# compare the actual values for a slice of last dim
													self.assertTrue(torch.allclose(output[:, :, -1]       ,     __magic_name__       ,     atol=1E-3   )   )



							@slow
							def     UpperCAmelCase__	(							self							:     str   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  XLMRobertaModel.from_pretrained('''xlm-roberta-large'''   )
													UpperCAmelCase_			:      List[Any]     						=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
													# The dog is cute and lives in the garden house

													UpperCAmelCase_			:      Tuple     						=  torch.Size((1, 12, 10_24)   )  # batch_size, sequence_length, embedding_vector_dim
													UpperCAmelCase_			:      Tuple     						=  torch.tensor(
													    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]   )
													#  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_			:      int     						=  model(__magic_name__   )['''last_hidden_state'''].detach()
													self.assertEqual(output.shape       ,     __magic_name__   )
													# compare the actual values for a slice of last dim
													self.assertTrue(torch.allclose(output[:, :, -1]       ,     __magic_name__       ,     atol=1E-3   )   )












'''simple docstring'''







class 					__a      :

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  size
													UpperCAmelCase_			:      Tuple     						=  [0] * size
													UpperCAmelCase_			:      Optional[Any]     						=  [0] * size

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return index | (index + 1)

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return (index & (index + 1)) - 1

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  value
													while index < self.size:
																			UpperCAmelCase_			:      str     						=  self.get_prev(__magic_name__   ) + 1
																			if current_left_border == index:
																									UpperCAmelCase_			:      List[str]     						=  value
																			else:
																									UpperCAmelCase_			:      Optional[int]     						=  max(__magic_name__       ,     __magic_name__       ,     __magic_name__   )
																			UpperCAmelCase_			:      Tuple     						=  self.get_next(__magic_name__   )



							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       int:







													"""simple docstring"""
													right -= 1  # Because of right is exclusive
													UpperCAmelCase_			:      List[str]     						=  0
													while left <= right:
																			UpperCAmelCase_			:      Optional[Any]     						=  self.get_prev(__magic_name__   )
																			if left <= current_left:
																									UpperCAmelCase_			:      Dict     						=  max(__magic_name__       ,     self.tree[right]   )
																									UpperCAmelCase_			:      Optional[Any]     						=  current_left
																			else:
																									UpperCAmelCase_			:      str     						=  max(__magic_name__       ,     self.arr[right]   )
																									right -= 1
													return result


if __name__ == "__main__":
					import doctest

					doctest.testmod()





