



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 		_lowercase		(							unittest.TestCase						):

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


    """simple docstring"""






    a							=		CamembertModel.from_pretrained("camembert-base"				)
    model.to(__lowerCAmelCase				)

    a							=		torch.tensor(
        [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]				, device=__lowerCAmelCase				, dtype=torch.long				, )  # J'aime le camembert !
    with torch.no_grad():
     a							=		model(__lowerCAmelCase				)["""last_hidden_state"""]
    a							=		torch.Size((1, 10, 768)				)
    self.assertEqual(output.shape				, __lowerCAmelCase				)
    # compare the actual values for a slice.
    a							=		torch.tensor(
        [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]				, 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				)				)





'''simple docstring'''
def 				lowerCAmelCase__	(       UpperCAmelCase = 30     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					exponent + 2
       snake_case__					:	List[Any]          =					(1 + 5**0.5) / 2
       snake_case__					:	Tuple          =					(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

       return int(UpperCAmelCase     )


if __name__ == "__main__":
     print(f"""{solution() = }""")




