


"""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 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     XLMRobertaModel.from_pretrained("xlm-roberta-base"    )
     _lowerCamelCase   :				Tuple							=     torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]    )
     # The dog is cute and lives in the garden house

     _lowerCamelCase   :				str							=     torch.Size((1, 12, 768)    )  # batch_size, sequence_length, embedding_vector_dim
     _lowerCamelCase   :				List[str]							=     torch.tensor(
         [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]    )
     #  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   :				List[str]							=     model(__lowerCAmelCase    )["last_hidden_state"].detach()
     self.assertEqual(output.shape							,__lowerCAmelCase    )
     # compare the actual values for a slice of last dim
     self.assertTrue(torch.allclose(output[:, :, -1]							,__lowerCAmelCase							,atol=1e-3    )    )






  @slow
  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     XLMRobertaModel.from_pretrained("xlm-roberta-large"    )
     _lowerCamelCase   :				Optional[int]							=     torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]    )
     # The dog is cute and lives in the garden house

     _lowerCamelCase   :				str							=     torch.Size((1, 12, 1_024)    )  # batch_size, sequence_length, embedding_vector_dim
     _lowerCamelCase   :				Optional[int]							=     torch.tensor(
         [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]    )
     #  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   :				Tuple							=     model(__lowerCAmelCase    )["last_hidden_state"].detach()
     self.assertEqual(output.shape							,__lowerCAmelCase    )
     # compare the actual values for a slice of last dim
     self.assertTrue(torch.allclose(output[:, :, -1]							,__lowerCAmelCase							,atol=1e-3    )    )

"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					, __a = None					, __a = None					):

					'''simple docstring'''





					if start is None:
										A_								=    0

					if end is None:
										A_								=    len(__a					) - 1

					if start >= end:
										return

					A_								=    (start + end) // 2

					slowsort(__a					, __a					, __a					)
					slowsort(__a					, mid + 1					, __a					)

					if sequence[end] < sequence[mid]:
										A_			,     A_								=    sequence[mid], sequence[end]

					slowsort(__a					, __a					, end - 1					)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




