





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 			__UpperCamelCase    (							unittest.TestCase    ):





     @slow
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''					)

        _UpperCAmelCase           =   tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]				,							dtype=tf.intaa				,							)  # J'aime le camembert !"

        _UpperCAmelCase           =   model(_UpperCamelCase					)['''last_hidden_state''']
        _UpperCAmelCase           =   tf.TensorShape((1, 10, 768)					)
        self.assertEqual(output.shape				,							_UpperCamelCase					)
        # compare the actual values for a slice.
        _UpperCAmelCase           =   tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]				,							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       a_ (	lowercase__ :str,			lowercase__ :int     ):
			return [sentence[i : i + ngram_size] for i in range(len(lowercase__     ) - ngram_size + 1     )]


if __name__ == "__main__":
					from doctest import testmod

					testmod()




