


"""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 _lowerCAmelCase    (				unittest.TestCase     ):







   @slow
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				str				 =   TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"						)

       snake_case   :				Optional[int]				 =   {
           "input_ids": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 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   :				Tuple				 =   model(UpperCamelCase__						)["last_hidden_state"]
       snake_case   :				Any				 =   tf.TensorShape((1, 6, 768)						)
       self.assertEqual(output.shape						,     UpperCamelCase__						)
       # compare the actual values for a slice.
       snake_case   :				List[Any]				 =   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   __lowerCAmelCase						(						lowercase  :  list[float]			)     ->						Any:


    """simple docstring"""





    return np.maximum(0						,							lowercase			)


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





