




'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class    __lowercase						(  unittest.TestCase ):







   @slow
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

     __a			=					tf.convert_to_tensor(
         [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]      ,							dtype=tf.intaa      ,							)  # J'aime le camembert !"

     __a			=					model(UpperCamelCase      )['last_hidden_state']
     __a			=					tf.TensorShape((1, 10, 768)      )
     self.assertEqual(output.shape      ,							UpperCamelCase      )
     # compare the actual values for a slice.
     __a			=					tf.convert_to_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]]]      ,							dtype=tf.floataa      ,							)
     # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
     # camembert.eval()
     # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

     self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,							expected_slice.numpy()      ,							atol=1e-4      )      )









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int			):
  return int((input_a, input_a).count(0			) != 0			)


def 						SCREAMING_SNAKE_CASE       (      ):
  assert nand_gate(0				,					0			) == 1
  assert nand_gate(0				,					1			) == 1
  assert nand_gate(1				,					0			) == 1
  assert nand_gate(1				,					1			) == 0


if __name__ == "__main__":
  print(nand_gate(0, 0))
  print(nand_gate(0, 1))
  print(nand_gate(1, 0))
  print(nand_gate(1, 1))



