





"""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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

  lowerCAmelCase    				= tf.convert_to_tensor(
      [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]				,       dtype=tf.intaa				,       )  # J'aime le camembert !"

  lowerCAmelCase    				= model(_snake_case     )['last_hidden_state']
  lowerCAmelCase    				= tf.TensorShape((1, 10, 7_68)     )
  self.assertEqual(output.shape				,       _snake_case     )
  # compare the actual values for a slice.
  lowerCAmelCase    				= 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			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





