

'''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   ):




  """simple docstring"""




  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				XLMRobertaModel.from_pretrained('''xlm-roberta-base'''    )
   __lowercase													=				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

   __lowercase													=				torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
   __lowercase													=				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():
    __lowercase													=				model(lowercase__    )['''last_hidden_state'''].detach()
   self.assertEqual(output.shape      ,lowercase__    )
   # compare the actual values for a slice of last dim
   self.assertTrue(torch.allclose(output[:, :, -1]      ,lowercase__      ,atol=1e-3    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				XLMRobertaModel.from_pretrained('''xlm-roberta-large'''    )
   __lowercase													=				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

   __lowercase													=				torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
   __lowercase													=				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():
    __lowercase													=				model(lowercase__    )['''last_hidden_state'''].detach()
   self.assertEqual(output.shape      ,lowercase__    )
   # compare the actual values for a slice of last dim
   self.assertTrue(torch.allclose(output[:, :, -1]      ,lowercase__      ,atol=1e-3    )    )




'''simple docstring'''






def 	_A  (					):






 """simple docstring"""



 for n in range(1  ,       1000000   ):
  yield n * (n + 1) // 2


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				1
 __lowercase													=				2
 while i * i <= n:
  __lowercase													=				0
  while n % i == 0:
   n //= i
   multiplicity += 1
  divisors_count *= multiplicity + 1
  i += 1
 if n > 1:
  divisors_count *= 2
 return divisors_count


def 	_A  (					):






 """simple docstring"""



 return next(i for i in triangle_number_generator() if count_divisors(A__   ) > 500   )


if __name__ == "__main__":
 print(solution())

