

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):




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Dict		=     CamembertModel.from_pretrained('camembert-base'							)
   model.to(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     torch.tensor(
       [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,  device=_UpperCAmelCase  ,  dtype=torch.long  ,  )  # J'aime le camembert !
   with torch.no_grad():
     __UpperCAmelCase	:  List[Any]		=     model(_UpperCAmelCase							)['last_hidden_state']
   __UpperCAmelCase	:  str		=     torch.Size((1, 10, 768)							)
   self.assertEqual(output.shape  ,  _UpperCAmelCase							)
   # compare the actual values for a slice.
   __UpperCAmelCase	:  Optional[int]		=     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=_UpperCAmelCase  ,  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]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)








def  _snake_case		(snake_case_					:     float  )							->		int:
  if num < 0:
    return -1
  return 1 if num else 0





def  _snake_case		()							->		None:
  assert signum(5  ) == 1
  assert signum(-5  ) == -1
  assert signum(0  ) == 0


if __name__ == "__main__":
      print(signum(1_2))
      print(signum(-1_2))
      print(signum(0))





