
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        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    TFXLMRobertaModel.from_pretrained('jplu/tf-xlm-roberta-base'       )

		lowercase       =    {
		    '''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       ),
		}

		lowercase       =    model(UpperCamelCase_       )['''last_hidden_state''']
		lowercase       =    tf.TensorShape((1, 6, 768)       )
		self.assertEqual(output.shape		,    UpperCamelCase_       )
		# compare the actual values for a slice.
		lowercase       =    tf.convert_to_tensor(
		    [
		        [
		            [0.0_681_762, 0.10_894_451, 0.06_772_504],
		            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
		            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
		        ]
		    ]		,    dtype=tf.floataa		,    )

		self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,    expected_slice.numpy()		,    atol=1E-4       )       )





"""simple docstring"""





import numpy as np


def    _snake_case  (  _snake_case :	np.array	):
      return 1 / (1 + np.exp(-vector	))


if __name__ == "__main__":
 import doctest

 doctest.testmod()


