





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      ):




   """simple docstring"""



   @slow
   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          a__		:      Optional[Any]					=							XLMRobertaModel.from_pretrained('xlm-roberta-base')
          a__		:      Tuple					=							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

          a__		:      Tuple					=							torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          a__		:      int					=							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():
                 a__		:      Dict					=							model(__a)["last_hidden_state"].detach()
          self.assertEqual(output.shape   ,    __a)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]   ,    __a   ,    atol=1e-3))

   @slow
   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      str					=							XLMRobertaModel.from_pretrained('xlm-roberta-large')
          a__		:      List[Any]					=							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

          a__		:      Tuple					=							torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          a__		:      Union[str, Any]					=							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():
                 a__		:      str					=							model(__a)["last_hidden_state"].detach()
          self.assertEqual(output.shape   ,    __a)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]   ,    __a   ,    atol=1e-3))









"""simple docstring"""


from collections.abc import Callable

import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		np.ndarray:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							int(np.ceil((x_end - xa) / step_size      )      )
      _UpperCamelCase   :						Tuple    =							np.zeros((n + 1,)      )
      _UpperCamelCase   :						List[str]    =							ya
      _UpperCamelCase   :						List[Any]    =							xa

      for k in range(lowercase_      ):
            _UpperCamelCase   :						List[str]    =							y[k] + step_size * ode_func(lowercase_		,y[k]      )
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
