



'''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 TFXLMRobertaModel


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



     '''simple docstring'''






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"	)

       _snake_case     		=				{
           "input_ids": tf.convert_to_tensor([[0, 2_646, 10_269, 83, 99_942, 2]]						,   dtype=tf.intaa	),  # "My dog is cute"
           "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]						,   dtype=tf.intaa	),
       }

       _snake_case     		=				model(__lowerCAmelCase	)["last_hidden_state"]
       _snake_case     		=				tf.TensorShape((1, 6, 768)	)
       self.assertEqual(output.shape						,   __lowerCAmelCase	)
       # compare the actual values for a slice.
       _snake_case     		=				tf.convert_to_tensor(
           [
               [
                   [0.0681762, 0.10894451, 0.06772504],
                   [-0.06423668, 0.02366615, 0.04329344],
                   [-0.06057295, 0.09974135, -0.00070584],
               ]
           ]						,   dtype=tf.floataa						,   )

       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 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


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


