"""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  _A						(       unittest.TestCase	):

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""						)

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

            lowercase														=	model(__lowerCAmelCase						)["""last_hidden_state"""]
            lowercase														=	tf.TensorShape((1, 10, 768)						)
            self.assertEqual(output.shape		,       __lowerCAmelCase						)
            # compare the actual values for a slice.
            lowercase														=	tf.convert_to_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]]]		,       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"""






from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[float]		) ->    Optional[Any]:
      '''simple docstring'''


      return np.maximum(0					,		lowerCAmelCase__		)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



