





'''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 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	@slow
	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''	)

				__lowercase			  =							{
				    '''input_ids''': tf.convert_to_tensor([[0, 2646, 10269, 83, 99942, 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(_lowerCamelCase	)['''last_hidden_state''']
				__lowercase			  =							tf.TensorShape((1, 6, 768)	)
				self.assertEqual(output.shape       ,_lowerCamelCase	)
				# compare the actual values for a slice.
				__lowercase			  =							tf.convert_to_tensor(
				    [
				        [
				            [0.0_6_8_1_7_6_2, 0.1_0_8_9_4_4_5_1, 0.0_6_7_7_2_5_0_4],
				            [-0.0_6_4_2_3_6_6_8, 0.0_2_3_6_6_6_1_5, 0.0_4_3_2_9_3_4_4],
				            [-0.0_6_0_5_7_2_9_5, 0.0_9_9_7_4_1_3_5, -0.0_0_0_7_0_5_8_4],
				        ]
				    ]       ,dtype=tf.floataa       ,)

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












'''simple docstring'''



import numpy as np




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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					np.array			):
			return vector * sigmoid(1.7_02 * vector			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





