code: 
  import unittest
  
  from transformers import is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  
  
  if is_torch_available():
       import torch
  
       from transformers import CamembertModel
  
  
  
  @require_torch
  @require_sentencepiece
  @require_tokenizers
  class  lowerCAmelCase__   (       unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @slow
    def 		_lowerCamelCase	(self  : int    )   ->       str:
          snake_case_						       =							CamembertModel.from_pretrained("camembert-base"    )
          model.to(lowerCAmelCase_    )
  
          snake_case_						       =							torch.tensor(
              [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	, device=lowerCAmelCase_	, dtype=torch.long	, )  # J'aime le camembert !
          with torch.no_grad():
                snake_case_						       =							model(lowerCAmelCase_    )["last_hidden_state"]
          snake_case_						       =							torch.Size((1, 10, 7_68)    )
          self.assertEqual(output.shape	, lowerCAmelCase_    )
          # compare the actual values for a slice.
          snake_case_						       =							torch.tensor(
              [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]	, device=lowerCAmelCase_	, dtype=torch.float	, )
          # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
          # camembert.eval()
          # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
  
          self.assertTrue(torch.allclose(output[:, :3, :3]	, lowerCAmelCase_	, atol=1E-4    )    )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  def   snake_case_       (							_a     :							int = 1_000			):
        return sum(i for i in range(_a			) if i % 3 == 0 or i % 5 == 0			)
  
  
  if __name__ == "__main__":
       print(F"""{solution() = }""")
  
  