






'''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       snake_case__	( unittest.TestCase):




					@slow
					def 						A	(   self   :						List[Any]	)  ->				List[str]:
								UpperCAmelCase_	:    Union[str, Any]	  =    TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''	)

								UpperCAmelCase_	:    Any	  =    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 !"

								UpperCAmelCase_	:    str	  =    model(_A	)["""last_hidden_state"""]
								UpperCAmelCase_	:    Union[str, Any]	  =    tf.TensorShape((1, 10, 7_68)	)
								self.assertEqual(output.shape							,      _A	)
								# compare the actual values for a slice.
								UpperCAmelCase_	:    Dict	  =    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	)	)




def     _a					(						lowerCamelCase							):
 return " ".join(
     """""".join(word[::-1]							) if len(lowerCamelCase							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(reverse_long_words("""Hey wollef sroirraw"""))




